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  • 1.
    Alberti, Marina
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Chemical Science and Engineering (CHE).
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Relational approaches for joint object classification andscene similarity measurement in indoor environments2014In: Proc. of 2014 AAAI Spring Symposium QualitativeRepresentations for Robots 2014, Palo Alto, California: The AAAI Press , 2014Conference paper (Refereed)
    Abstract [en]

    The qualitative structure of objects and their spatial distribution,to a large extent, define an indoor human environmentscene. This paper presents an approach forindoor scene similarity measurement based on the spatialcharacteristics and arrangement of the objects inthe scene. For this purpose, two main sets of spatialfeatures are computed, from single objects and objectpairs. A Gaussian Mixture Model is applied both onthe single object features and the object pair features, tolearn object class models and relationships of the objectpairs, respectively. Given an unknown scene, the objectclasses are predicted using the probabilistic frameworkon the learned object class models. From the predictedobject classes, object pair features are extracted. A fi-nal scene similarity score is obtained using the learnedprobabilistic models of object pair relationships. Ourmethod is tested on a real world 3D database of deskscenes, using a leave-one-out cross-validation framework.To evaluate the effect of varying conditions on thescene similarity score, we apply our method on mockscenes, generated by removing objects of different categoriesin the test scenes.

  • 2.
    Almeida, Diogo
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH.
    Ambrus, Rares
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Caccamo, Sergio
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Chen, Xi
    KTH.
    Cruciani, Silvia
    Pinto Basto De Carvalho, Joao F
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Haustein, Joshua
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Marzinotto, Alejandro
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Vina, Francisco
    KTH.
    Karayiannidis, Yannis
    KTH.
    Ögren, Petter
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Team KTH’s Picking Solution for the Amazon Picking Challenge 20162017In: Warehouse Picking Automation Workshop 2017: Solutions, Experience, Learnings and Outlook of the Amazon Robotics Challenge, 2017Conference paper (Other (popular science, discussion, etc.))
    Abstract [en]

    In this work we summarize the solution developed by Team KTH for the Amazon Picking Challenge 2016 in Leipzig, Germany. The competition simulated a warehouse automation scenario and it was divided in two tasks: a picking task where a robot picks items from a shelf and places them in a tote and a stowing task which is the inverse task where the robot picks items from a tote and places them in a shelf. We describe our approach to the problem starting from a high level overview of our system and later delving into details of our perception pipeline and our strategy for manipulation and grasping. The solution was implemented using a Baxter robot equipped with additional sensors.

  • 3.
    Ambrus, Rares
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Bore, Nils
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH Royal Inst Technol, Ctr Autonomous Syst, SE-10044 Stockholm, Sweden..
    Autonomous meshing, texturing and recognition of object models with a mobile robot2017In: 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) / [ed] Bicchi, A Okamura, A, IEEE , 2017, p. 5071-5078Conference paper (Refereed)
    Abstract [en]

    We present a system for creating object models from RGB-D views acquired autonomously by a mobile robot. We create high-quality textured meshes of the objects by approximating the underlying geometry with a Poisson surface. Our system employs two optimization steps, first registering the views spatially based on image features, and second aligning the RGB images to maximize photometric consistency with respect to the reconstructed mesh. We show that the resulting models can be used robustly for recognition by training a Convolutional Neural Network (CNN) on images rendered from the reconstructed meshes. We perform experiments on data collected autonomously by a mobile robot both in controlled and uncontrolled scenarios. We compare quantitatively and qualitatively to previous work to validate our approach.

  • 4.
    Ambrus, Rares
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Bore, Nils
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Autonomous meshing, texturing and recognition of objectmodels with a mobile robot2017Conference paper (Refereed)
    Abstract [en]

    We present a system for creating object modelsfrom RGB-D views acquired autonomously by a mobile robot.We create high-quality textured meshes of the objects byapproximating the underlying geometry with a Poisson surface.Our system employs two optimization steps, first registering theviews spatially based on image features, and second aligningthe RGB images to maximize photometric consistency withrespect to the reconstructed mesh. We show that the resultingmodels can be used robustly for recognition by training aConvolutional Neural Network (CNN) on images rendered fromthe reconstructed meshes. We perform experiments on datacollected autonomously by a mobile robot both in controlledand uncontrolled scenarios. We compare quantitatively andqualitatively to previous work to validate our approach.

  • 5.
    Ambrus, Rares
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Bore, Nils
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Meta-rooms: Building and Maintaining Long Term Spatial Models in a Dynamic World2014In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2014), IEEE conference proceedings, 2014, p. 1854-1861Conference paper (Refereed)
    Abstract [en]

    We present a novel method for re-creating the static structure of cluttered office environments -which we define as the " meta-room" -from multiple observations collected by an autonomous robot equipped with an RGB-D depth camera over extended periods of time. Our method works directly with point clusters by identifying what has changed from one observation to the next, removing the dynamic elements and at the same time adding previously occluded objects to reconstruct the underlying static structure as accurately as possible. The process of constructing the meta-rooms is iterative and it is designed to incorporate new data as it becomes available, as well as to be robust to environment changes. The latest estimate of the meta-room is used to differentiate and extract clusters of dynamic objects from observations. In addition, we present a method for re-identifying the extracted dynamic objects across observations thus mapping their spatial behaviour over extended periods of time.

  • 6.
    Ambrus, Rares
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Ekekrantz, Johan
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Unsupervised learning of spatial-temporal models of objects in a long-term autonomy scenario2015In: 2015 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), IEEE , 2015, p. 5678-5685Conference paper (Refereed)
    Abstract [en]

    We present a novel method for clustering segmented dynamic parts of indoor RGB-D scenes across repeated observations by performing an analysis of their spatial-temporal distributions. We segment areas of interest in the scene using scene differencing for change detection. We extend the Meta-Room method and evaluate the performance on a complex dataset acquired autonomously by a mobile robot over a period of 30 days. We use an initial clustering method to group the segmented parts based on appearance and shape, and we further combine the clusters we obtain by analyzing their spatial-temporal behaviors. We show that using the spatial-temporal information further increases the matching accuracy.

  • 7.
    Ambrus, Rares
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Unsupervised object segmentation through change detection in a long term autonomy scenario2016In: IEEE-RAS International Conference on Humanoid Robots, IEEE, 2016, p. 1181-1187Conference paper (Refereed)
    Abstract [en]

    In this work we address the problem of dynamic object segmentation in office environments. We make no prior assumptions on what is dynamic and static, and our reasoning is based on change detection between sparse and non-uniform observations of the scene. We model the static part of the environment, and we focus on improving the accuracy and quality of the segmented dynamic objects over long periods of time. We address the issue of adapting the static structure over time and incorporating new elements, for which we train and use a classifier whose output gives an indication of the dynamic nature of the segmented elements. We show that the proposed algorithms improve the accuracy and the rate of detection of dynamic objects by comparing with a labelled dataset.

  • 8. Autin, D
    et al.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Using multiple gaussian hypotheses to represent probability distributions for mobile robot localization2000Conference paper (Refereed)
    Abstract [en]

    A new mobile robot localization technique is presented which uses multiple Gaussian hypotheses to represent the probability distribution of the robots location in the environment. A tree of hypotheses is built by the application of Bayes' rule with each new sensor mesurement. However, such a tree can grow without bound and so rules are introduced for the elimination of the least likely hypotheses from the tree and for the proper re-distribution of their probability. This technique is applied to a feature-based mobile robot localization scheme and experimental results are given demonstrating the effectiveness of the scheme.

  • 9.
    Aydemir, Alper
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Bishop, Adrian N.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Simultaneous Object Class and Pose Estimation for Mobile Robotic Applications with Minimalistic Recognition2010In: 2010 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)    / [ed] Rakotondrabe M; Ivan IA, 2010, p. 2020-2027Conference paper (Refereed)
    Abstract [en]

    In this paper we address the problem of simultaneous object class and pose estimation using nothing more than object class label measurements from a generic object classifier. We detail a method for designing a likelihood function over the robot configuration space. This function provides a likelihood measure of an object being of a certain class given that the robot (from some position) sees and recognizes an object as being of some (possibly different) class. Using this likelihood function in a recursive Bayesian framework allows us to achieve a kind of spatial averaging and determine the object pose (up to certain ambiguities to be made precise). We show how inter-class confusion from certain robot viewpoints can actually increase the ability to determine the object pose. Our approach is motivated by the idea of minimalistic sensing since we use only class label measurements albeit we attempt to estimate the object pose in addition to the class.

  • 10.
    Aydemir, Alper
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Göbelbecker, Moritz
    Institut für Informatik, Albert-Ludwigs-Universität Freiburg, Germany.
    Pronobis, Andrzej
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Sjöö, Kristoffer
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Plan-based Object Search and Exploration Using Semantic Spatial Knowledge in the Real World2011In: Proc. of the European Conference on Mobile Robotics (ECMR'11), 2011Conference paper (Refereed)
    Abstract [en]

    In this paper we present a principled planner based approach to the active visual object search problem in unknown environments. We make use of a hierarchical planner that combines the strength of decision theory and heuristics. Furthermore, our object search approach leverages on the conceptual spatial knowledge in the form of object cooccurences and semantic place categorisation. A hierarchical model for representing object locations is presented with which the planner is able to perform indirect search. Finally we present real world experiments to show the feasibility of the approach.

  • 11.
    Aydemir, Alper
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Henell, Daniel
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Shilkrot, R.
    Kinect@Home: Crowdsourcing a large 3D dataset of real environments2012In: AAAI Spring Symposium - Technical Report: Volume SS-12-06, 2012, 2012, Vol. SS-12-06, p. 8-9Conference paper (Refereed)
    Abstract [en]

    We present Kinect@Home, aimed at collecting a vast RGB-D dataset from real everyday living spaces. This dataset is planned to be the largest real world image collection of everyday environments to date, making use of the availability of a widely adopted robotics sensor which is also in the homes of millions of users, the Microsoft Kinect camera.

  • 12.
    Aydemir, Alper
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Exploiting and modeling local 3D structure for predicting object locations2012In: Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, IEEE , 2012, p. 3885-3892Conference paper (Refereed)
    Abstract [en]

    In this paper, we argue that there is a strong correlation between local 3D structure and object placement in everyday scenes. We call this the 3D context of the object. In previous work, this is typically hand-coded and limited to flat horizontal surfaces. In contrast, we propose to use a more general model for 3D context and learn the relationship between 3D context and different object classes. This way, we can capture more complex 3D contexts without implementing specialized routines. We present extensive experiments with both qualitative and quantitative evaluations of our method for different object classes. We show that our method can be used in conjunction with an object detection algorithm to reduce the rate of false positives. Our results support that the 3D structure surrounding objects in everyday scenes is a strong indicator of their placement and that it can give significant improvements in the performance of, for example, an object detection system. For evaluation, we have collected a large dataset of Microsoft Kinect frames from five different locations, which we also make publicly available.

  • 13.
    Aydemir, Alper
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    What can we learn from 38,000 rooms?: Reasoning about unexplored space in indoor environments2012In: Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, IEEE , 2012, p. 4675-4682Conference paper (Refereed)
    Abstract [en]

    Many robotics tasks require the robot to predict what lies in the unexplored part of the environment. Although much work focuses on building autonomous robots that operate indoors, indoor environments are neither well understood nor analyzed enough in the literature. In this paper, we propose and compare two methods for predicting both the topology and the categories of rooms given a partial map. The methods are motivated by the analysis of two large annotated floor plan data sets corresponding to the buildings of the MIT and KTH campuses. In particular, utilizing graph theory, we discover that local complexity remains unchanged for growing global complexity in real-world indoor environments, a property which we exploit. In total, we analyze 197 buildings, 940 floors and over 38,000 real-world rooms. Such a large set of indoor places has not been investigated before in the previous work. We provide extensive experimental results and show the degree of transferability of spatial knowledge between two geographically distinct locations. We also contribute the KTH data set and the software tools to with it.

  • 14.
    Aydemir, Alper
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Järleberg, Erik
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Prentice, S.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Predicting what lies ahead in the topology of indoor environments2012In: Spatial Cognition VIII: International Conference, Spatial Cognition 2012, Kloster Seeon, Germany, August 31 – September 3, 2012. Proceedings / [ed] Cyrill Stachniss, Kerstin Schill, David Uttal, Springer, 2012, p. 1-16Conference paper (Refereed)
    Abstract [en]

    A significant amount of research in robotics is aimed towards building robots that operate indoors yet there exists little analysis of how human spaces are organized. In this work we analyze the properties of indoor environments from a large annotated floorplan dataset. We analyze a corpus of 567 floors, 6426 spaces with 91 room types and 8446 connections between rooms corresponding to real places. We present a system that, given a partial graph, predicts the rest of the topology by building a model from this dataset. Our hypothesis is that indoor topologies consists of multiple smaller functional parts. We demonstrate the applicability of our approach with experimental results. We expect that our analysis paves the way for more data driven research on indoor environments.

  • 15.
    Aydemir, Alper
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Pronobis, Andrzej
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Gobelbecker, Moritz
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Active Visual Object Search in Unknown Environments Using Uncertain Semantics2013In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 29, no 4, p. 986-1002Article in journal (Refereed)
    Abstract [en]

    In this paper, we study the problem of active visual search (AVS) in large, unknown, or partially known environments. We argue that by making use of uncertain semantics of the environment, a robot tasked with finding an object can devise efficient search strategies that can locate everyday objects at the scale of an entire building floor, which is previously unknown to the robot. To realize this, we present a probabilistic model of the search environment, which allows for prioritizing the search effort to those parts of the environment that are most promising for a specific object type. Further, we describe a method for reasoning about the unexplored part of the environment for goal-directed exploration with the purpose of object search. We demonstrate the validity of our approach by comparing it with two other search systems in terms of search trajectory length and time. First, we implement a greedy coverage-based search strategy that is found in previous work. Second, we let human participants search for objects as an alternative comparison for our method. Our results show that AVS strategies that exploit uncertain semantics of the environment are a very promising idea, and our method pushes the state-of-the-art forward in AVS.

  • 16.
    Aydemir, Alper
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Sjöö, Kristoffer
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Pronobis, Andrzej
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Search in the real world: Active visual object search based on spatial relations2011In: IEEE International Conference on Robotics and Automation (ICRA), 2011, IEEE , 2011, p. 2818-2824Conference paper (Refereed)
    Abstract [en]

    Objects are integral to a robot’s understandingof space. Various tasks such as semantic mapping, pick-andcarrymissions or manipulation involve interaction with objects.Previous work in the field largely builds on the assumption thatthe object in question starts out within the ready sensory reachof the robot. In this work we aim to relax this assumptionby providing the means to perform robust and large-scaleactive visual object search. Presenting spatial relations thatdescribe topological relationships between objects, we thenshow how to use these to create potential search actions. Weintroduce a method for efficiently selecting search strategiesgiven probabilities for those relations. Finally we performexperiments to verify the feasibility of our approach.

  • 17.
    Aydemir, Alper
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Sjöö, Kristoffer
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Object search on a mobile robot using relational spatial information2010In: Proc. of the 11th Int Conference on Intelligent Autonomous Systems (IAS-11), Amsterdam: IOS Press, 2010, p. 111-120Conference paper (Refereed)
    Abstract [en]

    We present a method for utilising knowledge of qualitative spatial relations between objects in order to facilitate efficient visual search for those objects. A computational model for the relation is used to sample a probability distribution that guides the selection of camera views. Specifically we examine the spatial relation “on”, in the sense of physical support, and show its usefulness in search experiments on a real robot. We also experimentally compare different search strategies and verify the efficiency of so-called indirect search.

  • 18.
    Basiri, Meysam
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Bishop, Adrian N.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Distributed control of triangular formations with angle-only constraints2010In: Systems & control letters (Print), ISSN 0167-6911, E-ISSN 1872-7956, Vol. 59, no 2, p. 147-154Article in journal (Refereed)
    Abstract [en]

    This paper considers the coupled, bearing-only formation control of three mobile agents moving in the plane. Each agent has only local inter-agent bearing knowledge and is required to maintain a specified angular separation relative to both neighbor agents. Assuming that the desired angular separation of each agent relative to the group is feasible, a triangle is generated. The control law is distributed and accordingly each agent can determine their own control law using only the locally measured bearings. A convergence result is established in this paper which guarantees global asymptotic convergence of the formation to the desired formation shape.

  • 19.
    Basiri, Meysam
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Bishop, Adrian N.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Distributed Control of Triangular Sensor Formations with Angle-Only Constraints2009In: 2009 INTERNATIONAL CONFERENCE ON INTELLIGENT SENSORS, SENSOR NETWORKS AND INFORMATION PROCESSING (ISSNIP 2009), NEW YORK: IEEE , 2009, p. 121-126Conference paper (Refereed)
    Abstract [en]

    This paper considers the coupled formation control of three mobile agents moving in the plane. Each agent has only local inter-agent bearing knowledge and is required to maintain a specified angular separation relative to its neighbors. The problem considered in this paper differs from similar problems in the literature since no inter-agent distance measurements are employed and the desired formation is specified entirely by the internal triangle angles. Each agent's control law is distributed and based only on its locally measured bearings. A convergence result is established which guarantees global convergence of the formation to the desired formation shape.

  • 20.
    Bertolli, Federico
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Christensen, Henrik I.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    SLAM using visual scan-matching with distinguishable 3D points2006In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-12, NEW YORK: IEEE , 2006, p. 4042-4047Conference paper (Refereed)
    Abstract [en]

    Scan-matching based on data from a laser scanner is frequently used for mapping and localization. This paper presents an scan-matching approach based instead on visual information from a stereo system. The Scale Invariant Feature Transform (SIFT) is used together with epipolar constraints to get high matching precision between the stereo images. Calculating the 3D position of the corresponding points in the world results in a visual scan where each point has a descriptor attached to it. These descriptors can be used when matching scans acquired from different positions. Just like in the work with laser based scan matching a map can be defined as a set of reference scans and their corresponding acquisition point. In essence this reduces each visual scan that can consist of hundreds of points to a single entity for which only the corresponding robot pose has to be estimated in the map. This reduces the overall complexity of the map. The SIFT descriptor attached to each of the points in the reference allows for robust matching and detection of loop closing situations. The paper presents real-world experimental results from an indoor office environment.

  • 21.
    Bishop, Adrian
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Stochastically convergent localization of objects and actively controllable sensor-object pose2009In: Proceedings of 10th European Control Conference (ECC 2009), 2009Conference paper (Refereed)
    Abstract [en]

    The problem of object (network) localization using a mobile sensor is examined in this paper. Specifically, we consider a set of stationary objects located in the plane and a single mobile nonholonomic sensor tasked at estimating their relative position from range and bearing measurements. We derive a coordinate transform and a relative sensor-object motion model that leads to a novel problem formulation where the measurements are linear in the object positions. We then apply an extended Kalman filter-like algorithm to the estimation problem. Using stochastic calculus we provide an analysis of the convergence properties of the filter. We then illustrate that it is possible to steer the mobile sensor to achieve a relative sensor-object pose using a continuous control law. This last fact is significant since we circumvent Brockett's theorem and control the relative sensor-source pose using a simple controller.

  • 22.
    Bishop, Adrian N.
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    A Stochastically Stable Solution to the Problem of Robocentric Mapping2009In: ICRA: 2009 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, 2009, p. 1540-1547Conference paper (Refereed)
    Abstract [en]

    This paper provides a novel solution for robo-centric mapping using an autonomous mobile robot. The robot dynamic model is the standard unicycle model and the robot is assumed to measure both the range and relative bearing to the landmarks. The algorithm introduced in this paper relies on a coordinate transformation and an extended Kalman filter like algorithm. The coordinate transformation considered in this paper has not been previously considered for robocentric mapping applications. Moreover, we provide a rigorous stochastic stability analysis of the filter employed and we examine the conditions under which the mean-square estimation error converges to a steady-state value.

  • 23.
    Bishop, Adrian N.
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    An Optimality Analysis of Sensor-Target Geometries for Signal Strength Based Localization2009In: 2009 INTERNATIONAL CONFERENCE ON INTELLIGENT SENSORS, SENSOR NETWORKS AND INFORMATION PROCESSING (ISSNIP 2009), NEW YORK: IEEE , 2009, p. 127-132Conference paper (Refereed)
    Abstract [en]

    In this paper we characterize the bounds on localization accuracy in signal strength based localization. In particular, we provide a novel and rigorous analysis of the relative receiver-transmitter geometry and the effect of this geometry on the potential localization performance. We show that uniformly spacing sensors around the target is not optimal if the sensor-target ranges are not identical and is not necessary in any case. Indeed, we show that in general the optimal sensor-target geometry for signal strength based localization is not unique.

  • 24.
    Bishop, Adrian N.
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Stochastically convergent localization of objects by mobile sensors and actively controllable relative sensor-object2015In: 2009 European Control Conference, ECC 2009, 2015, p. 2384-2389Conference paper (Refereed)
    Abstract [en]

    The problem of object (network) localization using a mobile sensor is examined in this paper. Specifically, we consider a set of stationary objects located in the plane and a single mobile nonholonomic sensor tasked at estimating their relative position from range and bearing measurements. We derive a coordinate transform and a relative sensor-object motion model that leads to a novel problem formulation where the measurements are linear in the object positions. We then apply an extended Kalman filter-like algorithm to the estimation problem. Using stochastic calculus we provide an analysis of the convergence properties of the filter. We then illustrate that it is possible to steer the mobile sensor to achieve a relative sensor-object pose using a continuous control law. This last fact is significant since we circumvent Brockett's theorem and control the relative sensor-source pose using a simple controller.

  • 25. Bishop, A.N.
    et al.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Global Robot Localization with Random Finite Set Statistics2010In: Fusion 2010: 13th International Conference on Information Fusion, 2010, p. 5711873-Conference paper (Refereed)
    Abstract [en]

    We re-examine the problem of global localization of a robot using a rigorous Bayesian framework based on the idea of random finite sets. Random sets allow us to naturally develop a complete model of the underlying problem accounting for the statistics of missed detections and of spurious/erroneously detected (potentially unmodeled) features along with the statistical models of robot hypothesis disappearance and appearance. In addition, no explicit data association is required which alleviates one of the more difficult sub-problems. Following the derivation of the Bayesian solution, we outline its first-order statistical moment approximation, the so called probability hypothesis density filter. We present a statistical estimation algorithm for the number of potential robot hypotheses consistent with the accumulated evidence and we show how such an estimate can be used to aid in re-localization of kidnapped robots. We discuss the advantages of the random set approach and examine a number of illustrative simulations.

  • 26.
    Boberg, Anders
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Bishop, Adrian N.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Robocentric Mapping and Localization in Modified Spherical Coordinates with Bearing Measurements2009In: 2009 INTERNATIONAL CONFERENCE ON INTELLIGENT SENSORS, SENSOR NETWORKS AND INFORMATION PROCESSING (ISSNIP 2009), NEW YORK: IEEE , 2009, p. 139-144Conference paper (Refereed)
    Abstract [en]

    In this paper, a new approach to robotic mapping is presented that uses modified spherical coordinates in a robot-centered reference frame and a bearing-only measurement model. The algorithm provided in this paper permits robust delay-free state initialization and is computationally more efficient than the current standard in bearing-only (delay-free initialized) simultaneous localization and mapping (SLAM). Importantly, we provide a detailed nonlinear observability analysis which shows the system is generally observable. We also analyze the error convergence of the filter using stochastic stability analysis. We provide an explicit bound on the asymptotic mean state estimation error. A comparison of the performance of this filter is also made against a standard world-centric SLAM algorithm in a simulated environment.

  • 27.
    Bore, Nils
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Ambrus, Rares
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Efficient retrieval of arbitrary objects from long-term robot observations2017In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 91, p. 139-150Article in journal (Refereed)
    Abstract [en]

    We present a novel method for efficient querying and retrieval of arbitrarily shaped objects from large amounts of unstructured 3D point cloud data. Our approach first performs a convex segmentation of the data after which local features are extracted and stored in a feature dictionary. We show that the representation allows efficient and reliable querying of the data. To handle arbitrarily shaped objects, we propose a scheme which allows incremental matching of segments based on similarity to the query object. Further, we adjust the feature metric based on the quality of the query results to improve results in a second round of querying. We perform extensive qualitative and quantitative experiments on two datasets for both segmentation and retrieval, validating the results using ground truth data. Comparison with other state of the art methods further enforces the validity of the proposed method. Finally, we also investigate how the density and distribution of the local features within the point clouds influence the quality of the results.

  • 28.
    Bore, Nils
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Querying 3D Data by Adjacency Graphs2015In: Computer Vision Systems / [ed] Nalpantidis, Lazaros and Krüger, Volker and Eklundh, Jan-Olof and Gasteratos, Antonios, Springer Publishing Company, 2015, p. 243-252Chapter in book (Refereed)
    Abstract [en]

    The need for robots to search the 3D data they have saved is becoming more apparent. We present an approach for finding structures in 3D models such as those built by robots of their environment. The method extracts geometric primitives from point cloud data. An attributed graph over these primitives forms our representation of the surface structures. Recurring substructures are found with frequent graph mining techniques. We investigate if a model invariant to changes in size and reflection using only the geometric information of and between primitives can be discriminative enough for practical use. Experiments confirm that it can be used to support queries of 3D models.

  • 29.
    Bore, Nils
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Retrieval of Arbitrary 3D Objects From Robot Observations2015In: Retrieval of Arbitrary 3D Objects From Robot Observations, Lincoln: IEEE Robotics and Automation Society, 2015, p. 1-8Conference paper (Refereed)
    Abstract [en]

    We have studied the problem of retrieval of arbi-trary object instances from a large point cloud data set. Thecontext is autonomous robots operating for long periods of time,weeks up to months and regularly saving point cloud data. Theever growing collection of data is stored in a way that allowsranking candidate examples of any query object, given in theform of a single view point cloud, without the need to accessthe original data. The top ranked ones can then be compared ina second phase using the point clouds themselves. Our methoddoes not assume that the point clouds are segmented or that theobjects to be queried are known ahead of time. This means thatwe are able to represent the entire environment but it also posesproblems for retrieval. To overcome this our approach learnsfrom each actual query to improve search results in terms of theranking. This learning is automatic and based only on the queries.We demonstrate our system on data collected autonomously by arobot operating over 13 days in our building. Comparisons withother techniques and several variations of our method are shown.

  • 30. Caputo, B.
    et al.
    Pronobis, Andrzej
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Overview of the CLEF 2009 robot vision track2009In: CLEF2009 Working Notes: Working Notes for CLEF 2009 Workshop, co-located with the 13th European Conference on Digital Libraries (ECDL 2009), Corfù, Greece, September 30 - October 2, 2009 / [ed] Carol Peters, Nicola Ferro, CEUR-WS , 2009Conference paper (Refereed)
    Abstract [en]

    The robot vision task has been proposed to the ImageCLEF participants for the first time in 2009. The task attracted a considerable attention, with 19 inscribed research groups, 7 groups eventually participating and a total of 27 submitted runs. The task addressed the problem of visual place recognition applied to robot topological localization. Specifically, participants were asked to classify rooms on the basis of image sequences, captured by a perspective camera mounted on a mobile robot. The sequences were acquired in an office environment, under varying illumination conditions and across a time span of almost two years. The training and validation set consisted of a subset of the IDOL2 database1. The test set consisted of sequences similar to those in the training and validation set, but acquired 20 months later and imaging also additional rooms. Participants were asked to build a system able to answer the question "where are you?" (I am in the kitchen, in the corridor, etc) when presented with a test sequence imaging rooms seen during training, or additional rooms that were not imaged in the training sequence. The system had to assign each test image to one of the rooms present in the training sequence, or indicate that the image came from a new room. We asked all participants to solve the problem separately for each test image (obligatory task). Additionally, results could also be reported for algorithms exploiting the temporal continuity of the image sequences (optional task). Of the 27 runs, 21 were submitted to the obligatory task, and 6 to the optional task. The best result in the obligatory task was obtained by the Multimedia Information Retrieval Group of the University of Glasgow, UK with an approach based on local feature matching. The best result in the optional task was obtained by the Intelligent Systems and Data Mining Group (SIMD) of the University of Castilla-La Mancha, Albacete, Spain, with an approach based on local features and a particle filter.

  • 31. Egerstedt, Magnus
    et al.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    A control theoretic formulation of the generalized SLAM problem in robotics2008In: 2008 American Control Conference: Vols 1-12, 2008, p. 2409-2414Conference paper (Refereed)
    Abstract [en]

    Simultaneous Localization and Mapping (SLAM) has emerged as a key capability for autonomous mobile robots navigating in unknown environments. The basic idea behind SLAM is to concurrently obtain a map of the environment and an estimate of where the robot is placed within this map. In other words, the map and the robot's pose have to be estimated at the same time, given the same data set. This paper revisits this problem from a control theoretic vantage point by reformulating the SLAM problem as a problem of simultaneously estimating the state and the output map of a controlled, dynamical system. What is different with this formulation is that the map is contained in the output map and not, as previously done, in the state of the system.

  • 32.
    Ekekrantz, Johan
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Pronobis, Andrzej
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Adaptive Iterative Closest Keypoint2013In: 2013 European Conference on Mobile Robots, ECMR 2013 - Conference Proceedings, New York: IEEE , 2013, p. 80-87Conference paper (Refereed)
    Abstract [en]

    Finding accurate correspondences between overlapping 3D views is crucial for many robotic applications, from multi-view 3D object recognition to SLAM. This step, often referred to as view registration, plays a key role in determining the overall system performance. In this paper, we propose a fast and simple method for registering RGB-D data, building on the principle of the Iterative Closest Point (ICP) algorithm. In contrast to ICP, our method exploits both point position and visual appearance and is able to smoothly transition the weighting between them with an adaptive metric. This results in robust initial registration based on appearance and accurate final registration using 3D points. Using keypoint clustering we are able to utilize a non exhaustive search strategy, reducing runtime of the algorithm significantly. We show through an evaluation on an established benchmark that the method significantly outperforms current methods in both robustness and precision.

  • 33.
    Ekekrantz, Johan
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Thippur, Akshaya
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC).
    Probabilistic Primitive Refinement algorithm for colored point cloud data2015In: 2015 European Conference on Mobile Robots (ECMR), Lincoln: IEEE conference proceedings, 2015Conference paper (Refereed)
    Abstract [en]

    In this work we present the Probabilistic Primitive Refinement (PPR) algorithm, an iterative method for accurately determining the inliers of an estimated primitive (such as planes and spheres) parametrization in an unorganized, noisy point cloud. The measurement noise of the points belonging to the proposed primitive surface are modelled using a Gaussian distribution and the measurements of extraneous points to the proposed surface are modelled as a histogram. Given these models, the probability that a measurement originated from the proposed surface model can be computed. Our novel technique to model the noisy surface from the measurement data does not require a priori given parameters for the sensor noise model. The absence of sensitive parameters selection is a strength of our method. Using the geometric information obtained from such an estimate the algorithm then builds a color-based model for the surface, further boosting the accuracy of the segmentation. If used iteratively the PPR algorithm can be seen as a variation of the popular mean-shift algorithm with an adaptive stochastic kernel function.

  • 34.
    Ekvall, Staffan
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Integrating active mobile robot object recognition and SLAM in natural environments2006In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-12, NEW YORK: IEEE , 2006, p. 5792-5797Conference paper (Refereed)
    Abstract [en]

    Linking semantic and spatial information has become an important research area in robotics since, for robots interacting with humans and performing tasks in natural environments, it is of foremost importance to be able to reason beyond simple geometrical and spatial levels. In this paper, we consider this problem in a service robot scenario where a mobile robot autonomously navigates in a domestic environment, builds a map as it moves along, localizes its position in it, recognizes objects on its way and puts them in the map. The experimental evaluation is performed in a realistic setting where the main concentration is put on the synergy of object recognition and Simultaneous Localization and Mapping systems.

  • 35.
    Ekvall, Staffan
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Object detection and mapping for service robot tasks2007In: Robotica (Cambridge. Print), ISSN 0263-5747, E-ISSN 1469-8668, Vol. 25, p. 175-187Article in journal (Refereed)
    Abstract [en]

    The problem studied in this paper is a mobile robot that autonomously navigates in a domestic: environment, builds a map as it moves along and localizes its position in it. In addition, the robot detects predefined objects, estimates their position in the environment and integrates this with the localization module to automatically put the objects in the generated map. Thus, we demonstrate one of the possible strategies for the integration of spatial and semantic knowledge in a service robot scenario where a simultaneous localization and mapping (SLAM) and object detection/ recognition system work in synergy to provide a richer representation of the environment than it would be possible with either of the methods alone. Most SLAM systems build maps that are only used for localizing the robot. Such maps are typically based on grids or different types of features such as point and lines. The novelty is the augmentation of this process with an object-recognition system that detects objects in the environment and puts them in the map generated by the SLAM system. The metric map is also split into topological entities corresponding to rooms. In this way, the user can command the robot to retrieve a certain object from a certain room. We present the results of map building and an extensive evaluation of the object detection algorithm performed in an indoor setting.

  • 36. Faeulhammer, Thomas
    et al.
    Ambrus, Rares
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Burbridge, Christopher
    Zillich, Micheal
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Hawes, Nick
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Vincze, Marcus
    Autonomous Learning of Object Models on a Mobile Robot2017In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 1, p. 26-33, article id 7393491Article in journal (Refereed)
    Abstract [en]

    In this article we present and evaluate a system which allows a mobile robot to autonomously detect, model and re-recognize objects in everyday environments. Whilst other systems have demonstrated one of these elements, to our knowledge we present the first system which is capable of doing all of these things, all without human interaction, in normal indoor scenes. Our system detects objects to learn by modelling the static part of the environment and extracting dynamic elements. It then creates and executes a view plan around a dynamic element to gather additional views for learning. Finally these views are fused to create an object model. The performance of the system is evaluated on publicly available datasets as well as on data collected by the robot in both controlled and uncontrolled scenarios.

  • 37.
    Folkesson, John
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Christensen, Henrik
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Graphical SLAM using vision and the measurement subspace2005In: 2005 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-4, IEEE conference proceedings, 2005, p. 325-330Conference paper (Refereed)
    Abstract [en]

    In this paper we combine a graphical approach for simultaneous localization and mapping, SLAM, with a feature representation that addresses symmetries and constraints in the feature coordinates, the measurement subspace, M-space. The graphical method has the advantages of delayed linearizations and soft commitment to feature measurement matching. It also allows large maps to be built up as a network of small local patches, star nodes. This local map net is then easier to work with. The formation of the star nodes is explicitly stable and invariant with all the symmetries of the original measurements. All linearization errors are kept small by using a local frame. The construction of this invariant star is made clearer by the M-space feature representation. The M-space allows the symmetries and constraints of the measurements to be explicitly represented. We present results using both vision and laser sensors.

  • 38.
    Folkesson, John
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Christensen, Henrik
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Vision SLAM in the Measurement Subspace2005In: 2005 IEEE International Conference on Robotics and Automation (ICRA), Vols 1-4  Book Series, 2005, p. 30-35Conference paper (Refereed)
    Abstract [en]

    In this paper we describe an approach to feature representation for simultaneous localization and mapping, SLAM. It is a general representation for features that addresses symmetries and constraints in the feature coordinates. Furthermore, the representation allows for the features to be added to the map with partial initialization. This is an important property when using oriented vision features where angle information can be used before their full pose is known. The number of the dimensions for a feature can grow with time as more information is acquired. At the same time as the special properties of each type of feature are accounted for, the commonalities of all map features are also exploited to allow SLAM algorithms to be interchanged as well as choice of sensors and features. In other words the SLAM implementation need not be changed at all when changing sensors and features and vice versa. Experimental results both with vision and range data and combinations thereof are presented.

  • 39.
    Folkesson, John
    et al.
    Massacusetts Institute of Technology, Cambridge, MA .
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Christensen, Henrik I.
    Georgia Institute of Tech- nology, Atlanta, GA.
    The m-space feature representation for slam2007In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, ISSN 1552-3098, Vol. 23, no 5, p. 1024-1035Article in journal (Refereed)
    Abstract [en]

    In this paper, a new feature representation for simultaneous localization and mapping (SLAM) is discussed. The representation addresses feature symmetries and constraints explicitly to make the basic model numerically robust. In previous SLAM work, complete initialization of features is typically performed prior to introduction of a new feature into the map. This results in delayed use of new data. To allow early use of sensory data, the new feature representation addresses the use of features that initially have been partially observed. This is achieved by explicitly modelling the subspace of a feature that has been observed. In addition to accounting for the special properties of each feature type, the commonalities can be exploited in the new representation to create a feature framework that allows for interchanging of SLAM algorithms, sensor and features. Experimental results are presented using a low-cost Web-cam, a laser range scanner, and combinations thereof.

  • 40. Frintrop, S.
    et al.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Christensen, H.
    Simultaneous robot localization and mapping based on a visual attention system2007In: Attention in Cognitive Systems: Theories and Systems from an Interdisciplinary Viewpoint, 2007, p. 417-430Conference paper (Refereed)
    Abstract [en]

    Visual attention regions are useful for many applications in the field of computer vision and robotics. Here, we introduce an application to simultaneous robot localization and mapping. A biologically motivated attention system finds regions of interest which serve as visual landmarks for the robot. The regions are tracked and matched over consecutive frames to build stable landmarks and to estimate the 3D position of the landmarks in the environment. Matching of current landmarks to database entries enables loop closing and global localization. Additionally, the system is equipped with an active camera control, which supports the system with a tracking, a re-detection, and an exploration behaviour. We present experiments which show the applicability of the system in a real-world scenario. A comparison between the system operating in active and in passive mode shows the advantage of active camera control: we achieve a better distribution of landmarks as well as a faster and more reliable loop closing.

  • 41. Frintrop, Simone
    et al.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Active Gaze Control for Attentional Visual SLAM2008In: 2008 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, 2008, p. 3690-3697Conference paper (Refereed)
    Abstract [en]

    In this paper, we introduce an approach to active camera control for visual SLAM. Features, detected by a biologically motivated attention system, are tracked over several frames to determine stable landmarks. Matching of features to database entries enables global loop closing. The focus of this paper is the active camera control module, which supports the system with three behaviours: (i) A tracking behaviour tracks promising landmarks and prevents them from leaving the field of view, (ii) A redetection behaviour directs the camera actively to regions where landmarks are expected and thus supports loop closing, (iii) Finally, an exploration behaviour investigates regions without landmarks and enables a more uniform distribution of landmarks. Several real-world experiments show that the active camera control outperforms the passive system considerably.

  • 42. Frintrop, Simone
    et al.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Attentional Landmarks and Active Gaze Control for Visual SLAM2008In: IEEE Transactions on Robotics, special issue on visual SLAM, ISSN 1552-3098, Vol. 24, no 5, p. 1054-1065Article in journal (Refereed)
    Abstract [en]

    This paper is centered around landmark detection, tracking, and matching for visual simultaneous localization and mapping using a monocular vision system with active gaze control. We present a system that specializes in creating and maintaining a sparse set of landmarks based on a biologically motivated feature-selection strategy. A visual attention system detects salient features that are highly discriminative and ideal candidates for visual landmarks that are easy to redetect. Features are tracked over several frames to determine stable landmarks and to estimate their 3-D position in the environment. Matching of current landmarks to database entries enables loop closing. Active gaze control allows us to overcome some of the limitations of using a monocular vision system with a relatively small field of view. It supports 1) the tracking of landmarks that enable a better pose estimation, 2) the exploration of regions without landmarks to obtain a better distribution of landmarks in the environment, and 3) the active redetection of landmarks to enable loop closing in situations in which a fixed camera fails to close the loop. Several real-world experiments show that accurate pose estimation is obtained with the presented system and that active camera control outperforms the passive approach.

  • 43.
    Frintrop, Simone
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Christensen, Henrik I.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Attentional landmark selection for visual SLAM2006In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-12, NEW YORK: IEEE , 2006, p. 2582-2587Conference paper (Refereed)
    Abstract [en]

    In this paper, we introduce a new method to automatically detect useful landmarks for visual SLAM. A biologically motivated attention system detects regions of interest which "pop-out" automatically due to strong contrasts and the uniqueness of features. This property makes the regions easily redetectable and thus they are useful candidates for visual landmarks. Matching based on scene prediction and feature similarity allows not only short-term tracking of the regions, but also redetection in loop closing situations. The paper demonstrates how regions are determined and how they are matched reliably. Various experimental results on real-world data show that the landmarks are useful with respect to be tracked in consecutive frames and to enable closing loops.

  • 44.
    Frintrop, Simone
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Christensen, Henrik I.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Pay attention when selecting features2006In: 18th International Conference on Pattern Recognition, Vol 2, Proceedings / [ed] Tang, YY; Wang, SP; Lorette, G; Yeung, DS; Yan, H, 2006, p. 163-166Conference paper (Refereed)
    Abstract [en]

    In this paper we propose anew, hierarchical approach to landmark selection for simultaneous robot localization and mapping based on visual sensors: a biologically motivated attention system finds salient regions of interest (ROIs) in images, and within these regions, Harris corners are detected. This combines the advantages of the ROIs (reducing complexity, enabling good redetactability of regions) with the advantages of the Harris corners (high stability). Reducing complexity is important to meet real-time requirements and stability of features is essential to compute the depth of landmarks from structure from motion with a small baseline. We show that the number of landmarks is highly reduced compared to all Harris corners while maintaining the stability of features for the mapping task.

  • 45. Förell, Erik
    et al.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Robotsystem och förfarande för behandling av en yta2003Patent (Other (popular science, discussion, etc.))
    Abstract [en]

    Robot system including at least one mobile robot (10), for treating a surface, which comprises map storage means to store a map of the surface to be treated and means to navigate the, or each, mobile robot (10) to at least one point on a surface. The, or each, mobile robot (10) comprises locating means (13,14) to identify its position with respect to the surface to be treated and means t o automatically deviate the mobile robot (10) away from its initial path in the event that an obstacle is detected along its path. The, or each, mobile robot (10) also comprises means to store and/or communicate data concerning the surface treatment performed and any obstacles detected by the locating means (13,14).

  • 46.
    Gálvez López, Dorian
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Sjöö, Kristoffer
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Paul, Chandana
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Hybrid Laser and Vision Based Object Search and Localization2008In: 2008 IEEE International Conference on Robotics and Automation: Vols 1-9, 2008, p. 2636-2643Conference paper (Refereed)
    Abstract [en]

    We describe a method for an autonomous robot to efficiently locate one or more distinct objects in a realistic environment using monocular vision. We demonstrate how to efficiently subdivide acquired images into interest regions for the robot to zoom in on, using receptive field cooccurrence histograms. Objects are recognized through SIFT feature matching and the positions of the objects are estimated. Assuming a 2D map of the robot's surroundings and a set of navigation nodes between which it is free to move, we show how to compute an efficient sensing plan that allows the robot's camera to cover the environment, while obeying restrictions on the different objects' maximum and minimum viewing distances. The approach has been implemented on a real robotic system and results are presented showing its practicability and the quality of the position estimates obtained.

  • 47. Göbelbecker, M.
    et al.
    Aydemir, Alper
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Pronobis, Andrzej
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Sjöö, Kristoffer
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    A planning approach to active visual search in large environments2011In: AAAI Workshop Tech. Rep., 2011, p. 8-13Conference paper (Refereed)
    Abstract [en]

    In this paper we present a principled planner based approach to the active visual object search problem in unknown environments. We make use of a hierarchical planner that combines the strength of decision theory and heuristics. Furthermore, our object search approach leverages on the conceptual spatial knowledge in the form of object co-occurrences and semantic place categorisation. A hierarchical model for representing object locations is presented with which the planner is able to perform indirect search. Finally we present real world experiments to show the feasibility of the approach.

  • 48.
    Göransson, Rasmus
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Aydemir, A.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Kinect@home: A crowdsourced RGB-D dataset2016In: 13th International Conference on Intelligent Autonomous Systems, IAS 2014, Springer, 2016, Vol. 302, p. 843-858Conference paper (Refereed)
    Abstract [en]

    Algorithms for 3D localization, mapping, and reconstruction are getting increasingly mature. It is time to also make the datasets on which they are tested more realistic to reflect the conditions in the homes of real people. Today algorithms are tested on data gathered in the lab or at best in a few places, and almost always by the people that designed the algorithm. In this paper, we present the first RGB-D dataset from the crowd sourced data collection project Kinect@Home and perform an initial analysis of it. The dataset contains 54 recordings with a total of approximately 45 min of RGB-D video. We present a comparison of two different pose estimation methods, the Kinfu algorithm and a key point-based method, to show how this dataset can be used even though it is lacking ground truth. In addition, the analysis highlights the different characteristics and error modes of the two methods and shows how challenging data from the real world is.

  • 49.
    Hanheide, Marc
    et al.
    University of Lincoln.
    Göbelbecker, Moritz
    University of Freiburg.
    Horn, Graham S.
    University of Birmingham.
    Pronobis, Andrzej
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Sjöö, Kristoffer
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. krsj@kth.se.
    Aydemir, Alper
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Gretton, Charles
    University of Birmingham.
    Dearden, Richard
    University of Birmingham.
    Janicek, Miroslav
    DFKI, Saarbrücken.
    Zender, Hendrik
    DFKI, Saarbrücken.
    Kruijff, Geert-Jan
    DFKI, Saarbrücken.
    Hawes, Nick
    University of Birmingham.
    Wyatt, Jeremy
    University of Birmingham.
    Robot task planning and explanation in open and uncertain worlds2015In: Artificial Intelligence, ISSN 0004-3702, E-ISSN 1872-7921Article in journal (Refereed)
    Abstract [en]

    A long-standing goal of AI is to enable robots to plan in the face of uncertain and incomplete information, and to handle task failure intelligently. This paper shows how to achieve this. There are two central ideas. The first idea is to organize the robot's knowledge into three layers: instance knowledge at the bottom, commonsense knowledge above that, and diagnostic knowledge on top. Knowledge in a layer above can be used to modify knowledge in the layer(s) below. The second idea is that the robot should represent not just how its actions change the world, but also what it knows or believes. There are two types of knowledge effects the robot's actions can have: epistemic effects (I believe X because I saw it) and assumptions (I'll assume X to be true). By combining the knowledge layers with the models of knowledge effects, we can simultaneously solve several problems in robotics: (i) task planning and execution under uncertainty; (ii) task planning and execution in open worlds; (iii) explaining task failure; (iv) verifying those explanations. The paper describes how the ideas are implemented in a three-layer architecture on a mobile robot platform. The robot implementation was evaluated in five different experiments on object search, mapping, and room categorization.

  • 50.
    Hanheide, Marc
    et al.
    University of Birmingham.
    Hawes, Nick
    University of Birmingham.
    Wyatt, Jeremy
    University of Birmingham.
    Göbelbecker, Moritz
    Albert-Ludwigs-Universität.
    Brenner, Michael
    Albert-Ludwigs-Universität, Freiburg.
    Sjöö, Kristoffer
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Aydemir, Alper
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Zender, Hendrik
    DFKI Saarbrücken.
    Kruijff, Geert-Jan
    DFKI Saarbrücken.
    A Framework for Goal Generation and Management2010In: Proceedings of the AAAI Workshop on Goal-Directed Autonomy, 2010Conference paper (Refereed)
    Abstract [en]

    Goal-directed behaviour is often viewed as an essential char- acteristic of an intelligent system, but mechanisms to generate and manage goals are often overlooked. This paper addresses this by presenting a framework for autonomous goal gener- ation and selection. The framework has been implemented as part of an intelligent mobile robot capable of exploring unknown space and determining the category of rooms au- tonomously. We demonstrate the efficacy of our approach by comparing the performance of two versions of our inte- grated system: one with the framework, the other without. This investigation leads us conclude that such a framework is desirable for an integrated intelligent system because it re- duces the complexity of the problems that must be solved by other behaviour-generation mechanisms, it makes goal- directed behaviour more robust in the face of a dynamic and unpredictable environments, and it provides an entry point for domain-specific knowledge in a more general system.

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