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  • 1.
    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.

  • 2.
    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.

  • 3.
    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.

  • 4. 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.

  • 5.
    Chung, Michael Jae-Yoon
    et al.
    University of Washington, Seattle.
    Pronobis, Andrzej
    University of Washington, Seattle.
    Cakmak, Maya
    University of Washington, Seattle.
    Fox, Dieter
    University of Washington, Seattle.
    Rao, Rajesh P. N.
    University of Washington, Seattle.
    Autonomous Question Answering with Mobile Robots in Human-Populated Environments2016In: Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’16), IEEE, 2016Conference paper (Refereed)
    Abstract [en]

    Autonomous mobile robots will soon become ubiquitous in human-populated environments. Besides their typical applications in fetching, delivery, or escorting, such robots present the opportunity to assist human users in their daily tasks by gathering and reporting up-to-date knowledge about the environment. In this paper, we explore this use case and present an end-to-end framework that enables a mobile robot to answer natural language questions about the state of a large-scale, dynamic environment asked by the inhabitants of that environment. The system parses the question and estimates an initial viewpoint that is likely to contain information for answering the question based on prior environment knowledge. Then, it autonomously navigates towards the viewpoint while dynamically adapting to changes and new information. The output of the system is an image of the most relevant part of the environment that allows the user to obtain an answer to their question. We additionally demonstrate the benefits of a continuously operating information gathering robot by showing how the system can answer retrospective questions about the past state of the world using incidentally recorded sensory data. We evaluate our approach with a custom mobile robot deployed in a university building, with questions collected from occupants of the building. We demonstrate our system's ability to respond to these questions in different environmental conditions.

  • 6.
    Chung, Michael Jae-Yoon
    et al.
    University of Washington, Seattle.
    Pronobis, Andrzej
    University of Washington, Seattle.
    Cakmak, Maya
    University of Washington, Seattle.
    Fox, Dieter
    University of Washington, Seattle.
    Rao, Rajesh P. N.
    University of Washington, Seattle.
    Designing Information Gathering Robots for Human-Populated Environments2015In: Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’15), IEEE, 2015Conference paper (Refereed)
    Abstract [en]

    Advances in mobile robotics have enabled robots that can autonomously operate in human-populated environments. Although primary tasks for such robots might be fetching, delivery, or escorting, they present an untapped potential as information gathering agents that can answer questions for the community of co-inhabitants. In this paper, we seek to better understand requirements for such information gathering robots (InfoBots) from the perspective of the user requesting the information. We present findings from two studies: (i) a user survey conducted in two office buildings and (ii) a 4-day long deployment in one of the buildings, during which inhabitants of the building could ask questions to an InfoBot through a web-based interface. These studies allow us to characterize the types of information that InfoBots can provide for their users.

  • 7.
    Chung, Michael Jae-Yoon
    et al.
    University of Washington, Seattle.
    Pronobis, Andrzej
    University of Washington, Seattle.
    Cakmak, Maya
    University of Washington, Seattle.
    Fox, Dieter
    University of Washington, Seattle.
    Rao, Rajesh P. N.
    University of Washington, Seattle.
    Exploring the Potential of Information Gathering Robots2015In: Proceedings of the 10th Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts (HRI’15), ACM Digital Library, 2015Conference paper (Refereed)
    Abstract [en]

    Autonomous mobile robots equipped with a number of sensors will soon be ubiquitous in human populated environments. In this paper we present an initial exploration into the potential of using such robots for information gathering. We present findings from a formative user survey and a 4-day long Wizard-of-Oz deployment of a robot that answers questions such as "Is there free food on the kitchen table?" Our studies allow us to characterize the types of information that InfoBots might be most useful for.

  • 8.
    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.

  • 9. 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.

  • 10.
    Göbelbecker, Moritz
    et al.
    University of Freiburg.
    Hanheide, Marc
    University of Lincoln.
    Gretton, Charles
    University of Birmingham.
    Hawes, Nick
    University of Birmingham.
    Pronobis, Andrzej
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Aydemir, Alper
    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.
    Kristoffer, Sjöö
    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, Saarbruecken.
    Dora: A Robot that Plans and Acts Under Uncertainty2012In: Proceedings of the 35th German Conference on Artificial Intelligence (KI’12), 2012Conference paper (Refereed)
    Abstract [en]

    Dealing with uncertainty is one of the major challenges when constructing autonomous mobile robots. The CogX project addressed key aspects of that by developing and implementing mechanisms for self-understanding and self-extension -- i.e. awareness of gaps in knowledge, and the ability to reason and act to fill those gaps. We discuss our robot called Dora, a showcase outcome of that project. Dora is able to perform a variety of search tasks in unexplored environments. One of the results of the project is the Dora robot, that can perform a variety of search tasks in unexplored environments by exploiting probabilistic knowledge representations while retaining efficiency by using a fast planning system.

  • 11. Hanheide, Marc
    et al.
    Gretton, Charles
    Dearden, Richard
    Hawes, Nick
    Wyatt, Jeremy
    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.
    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.
    Göbelbecker, Moritz
    Zender, Hendrik
    Exploiting probabilistic knowledge under uncertain sensing for efficient robot behaviour2011In: 22nd International Joint Conference on Artificial Intelligence, 2011Conference paper (Refereed)
    Abstract [en]

    Robots must perform tasks efficiently and reliably while acting under uncertainty. One way to achieve efficiency is to give the robot common-sense knowledge about the structure of the world. Reliable robot behaviour can be achieved by modelling the uncertainty in the world probabilistically. We present a robot system that combines these two approaches and demonstrate the improvements in efficiency and reliability that result. Our first contribution is a probabilistic relational model integrating common-sense knowledge about the world in general, with observations of a particularenvironment. Our second contribution is a continual planning system which isable to plan in the large problems posed by that model, by automatically switching between decision-theoretic and classical procedures. We evaluate our system on objects earch tasks in two different real-world indoor environments. By reasoning about the trade-offs between possible courses of action with different informational effects, and exploiting the cues and general structures of those environments, our robot is able to consistently demonstrate efficient and reliable goal-directed behaviour.

  • 12.
    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.

  • 13. Luo, J.
    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.
    Caputo, B.
    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.
    Incremental learning for place recognition in dynamic environments2007In: Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on, IEEE , 2007, p. 721-728Conference paper (Refereed)
    Abstract [en]

    Vision-based place recognition is a desirable feature for an autonomous mobile system. In order to work in realistic scenarios, visual recognition algorithms should be adaptive, i.e. should be able to learn from experience and adapt continuously to changes in the environment. This paper presents a discriminative incremental learning approach to place recognition. We use a recently introduced version of the incremental SVM, which allows to control the memory requirements as the system updates its internal representation. At the same time, it preserves the recognition performance of the batch algorithm. In order to assess the method, we acquired a database capturing the intrinsic variability of places over time. Extensive experiments show the power and the potential of the approach.

  • 14.
    Luo, Jie
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Pronobis, Andrzej
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Caputo, Barbara
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    SVM-based Transfer of Visual Knowledge Across Robotic Platforms2007In: Proceedings of the 5th International Conference on Computer Vision Systems (ICVS’07), Applied Computer Science Group, Bielefeld University, Germany , 2007Conference paper (Refereed)
    Abstract [en]

    This paper presents an SVM-based algorithm for the transfer of knowledge across robot platforms aiming to perform the same task. Our method exploits efficiently the transferred knowledge while updating incrementally the internal representation as new information is available. The algorithm is adaptive and tends to privilege new data when building the SV solution. This prevents the old knowledge to nest into the model and eventually become a possible source of misleading information. We tested our approach in the domain of vision-based place recognition. Extensive experiments show that using transferred knowledge clearly pays off in terms of performance and stability of the solution.

  • 15.
    Maboudi Afkham, Heydar
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Tavakoli Targhi, Alireza
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Eklundh, Jan-Olof
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Pronobis, Andrzej
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Joint Visual Vocabulary For Animal Classification2008In: 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6, 2008, p. 2019-2022Conference paper (Refereed)
    Abstract [en]

    This paper presents a method for visual object categorization based on encoding the joint textural information in objects and the surrounding back-ground, and requiring no segmentation during recognition. The framework can be used together with various learning techniques and model representations. Here we use this framework with simple probabilistic models and more complex representations obtained using Support Vector Machines. We prove that our approach provides good recognition performance for complex problems for which some of the existing methods have difficulties. Additionally, we introduce a new extensive database containing realistic images of animals in complex natural environments. We asses the database in a set of experiments in which we compare the performance of our approach with a recently proposed method.

  • 16.
    Martínez-Gómez, Jesus
    et al.
    University of Castilla-La Mancha.
    Caputo, Barbara
    University of Rome La Sapienza.
    Cazorla, Miguel
    University of Alicante.
    Christensen, Henrik I.
    University of California, San Diego.
    Fornoni, Marco
    Idiap Research Institute and EPFL.
    García-Varea, Ismael
    University of Castilla-La Mancha.
    Pronobis, Andrzej
    University of Washington, United States.
    Where Are We After Five Editions?: Robot Vision Challenge, a Competition that Evaluates Solutions for the Visual Place Classification Problem2015In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 22, no 4, p. 147-156Article in journal (Refereed)
    Abstract [en]

    This article describes the Robot Vision challenge, a competition that evaluates solutions for the visual place classification problem. Since its origin, this challenge has been proposed as a common benchmark where worldwide proposals are measured using a common overall score. Each new edition of the competition introduced novelties, both for the type of input data and sub-objectives of the challenge. All the techniques used by the participants have been gathered up and published to make it accessible for future developments. The legacy of the Robot Vision challenge includes data sets, benchmarking techniques, and a wide experience in the place classification research that is reflected in this article.

  • 17.
    Pronobis, Andrzej
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Semantic Mapping with Mobile Robots2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    After decades of unrealistic predictions and expectations, robots have finally escaped from industrial workplaces and made their way into our homes,offices, museums and other public spaces. These service robots are increasingly present in our environments and many believe that it is in the area ofservice and domestic robotics that we will see the largest growth within thenext few years. In order to realize the dream of robot assistants performing human-like tasks together with humans in a seamless fashion, we need toprovide them with the fundamental capability of understanding complex, dynamic and unstructured environments. More importantly, we need to enablethem the sharing of our understanding of space to permit natural cooper-ation. To this end, this thesis addresses the problem of building internalrepresentations of space for artificial mobile agents populated with humanspatial semantics as well as means for inferring that semantics from sensoryinformation. More specifically, an extensible approach to place classificationis introduced and used for mobile robot localization as well as categorizationand extraction of spatial semantic concepts from general place appearance andgeometry. The models can be incrementally adapted to the dynamic changesin the environment and employ efficient ways for cue integration, sensor fu-sion and confidence estimation. In addition, a system and representationalapproach to semantic mapping is presented. The system incorporates and in-tegrates semantic knowledge from multiple sources such as the geometry andgeneral appearance of places, presence of objects, topology of the environmentas well as human input. A conceptual map is designed and used for modelingand reasoning about spatial concepts and their relations to spatial entitiesand their semantic properties. Finally, the semantic mapping algorithm isbuilt into an integrated robotic system and shown to substantially enhancethe performance of the robot on the complex task of active object search. Thepresented evaluations show the effectiveness of the system and its underlyingcomponents and demonstrate applicability to real-world problems in realistichuman settings.

  • 18.
    Pronobis, Andrzej
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Caputo, B
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Christensen, H. I.
    A realistic benchmark for visual indoor place recognition2010In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 58, no 1, p. 81-96Article in journal (Refereed)
    Abstract [en]

    An important competence for a mobile robot system is the ability to localize and perform context interpretation. This is required to perform basic navigation and to facilitate local specific services. Recent advances in vision have made this modality a viable alternative to the traditional range sensors, and visual place recognition algorithms emerged as a useful and widely applied tool for obtaining information about robot's position. Several place recognition methods have been proposed using vision alone or combined with sonar and/or laser. This research calls for standard benchmark datasets for development, evaluation and comparison of solutions. To this end, this paper presents two carefully designed and annotated image databases augmented with an experimental procedure and extensive baseline evaluation. The databases were gathered in an uncontrolled indoor office environment using two mobile robots and a standard camera. The acquisition spanned across a time range of several months and different illumination and weather conditions. Thus, the databases are very well suited for evaluating the robustness of algorithms with respect to a broad range of variations, often occurring in real-world settings. We thoroughly assessed the databases with a purely appearance-based place recognition method based on support vector machines and two types of rich visual features (global and local).

  • 19.
    Pronobis, Andrzej
    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.
    Caputo, Barbara
    COLD: The CoSy Localization Database2009In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 28, no 5, p. 588-594Article in journal (Refereed)
    Abstract [en]

    Two key competencies for mobile robotic systems are localization and semantic context interpretation. Recently, vision has become the modality of choice for these problems as it provides richer and more descriptive sensory input. At the same time, designing and testing vision-based algorithms still remains a challenge, as large amounts of carefully selected data are required to address the high variability of visual information. In this paper we present a freely available database which provides a large-scale, flexible testing environment for vision-based topological localization and semantic knowledge extraction in robotic systems. The database contains 76 image sequences acquired in three different indoor environments across Europe. Acquisition was performed with the same perspective and omnidirectional camera setup, in rooms of different functionality and under various conditions. The database is an ideal testbed for evaluating algorithms in real-world scenarios with respect to both dynamic and categorical variations.

  • 20.
    Pronobis, Andrzej
    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.
    Caputo, Barbara
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Confidence-based cue integration for visual place recognition2007In: 2007 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-9, NEW YORK: IEEE , 2007, p. 2400-2407Conference paper (Refereed)
    Abstract [en]

    A distinctive feature of intelligent systems is their capability to analyze their level of expertise for a given task; in other words, they know what they know. As a way towards this ambitious goal, this paper presents a recognition algorithm able to measure its own level of confidence and, in case of uncertainty, to seek for extra information so to increase its own knowledge and ultimately achieve better performance. We focus on the visual place recognition problem for topological localization, and we take an SVM approach. We propose a new method for measuring the confidence level of the classification output, based on the distance of a test image and the average distance of training vectors. This method is combined with a discriminative accumulation scheme for cue integration. We show with extensive experiments that the resulting algorithm achieves better performances for two visual cues than the classic single cue SVM on the same task, while minimising the computational load. More important, our method provides a reliable measure of the level of confidence of the decision.

  • 21.
    Pronobis, Andrzej
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Caputo, Barbara
    IDIAP Research Institute, Martigny, Switzerland.
    The robot vision task2010In: The Information Retrieval Series: ImageCLEF / [ed] H. Müller, P. Clough, T. Deselaers, B. Caputo, Springer Berlin/Heidelberg, 2010, 32, p. 185-198Chapter in book (Refereed)
    Abstract [en]

    In 2009, ImageCLEF expanded its tasks with the introduction of the first robot vision challenge. The overall focus of the challenge is semantic localization of a robot platform using visual place recognition. This is a key topic of research in the robotics community today. This chapter presents the goals and achievements of the first edition of the robot vision task. We describe the task, the method of data collection used and the evaluation procedure. We give an overview of the obtained results and briefly highlight the most promising approaches. We then outline how the task will evolve in the near and distant future.

  • 22.
    Pronobis, Andrzej
    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.
    Caputo, Barbara
    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.
    Christensen, Henrik I.
    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.
    A discriminative approach to robust visual place recognition2006In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-12, NEW YORK: IEEE , 2006, p. 3829-3836Conference paper (Refereed)
    Abstract [en]

    An important competence for a mobile robot system is the ability to localize and perform context interpretation. This is required to perform basic navigation and to facilitate local specific services. Usually localization is performed based on a purely geometric model. Through use of vision and place recognition a number of opportunities open up in terms of flexibility and association of semantics to the model. To achieve this the present paper presents an appearance based method for place recognition. The method is based on a large margin classifier in combination with a rich global image descriptor. The method is robust to variations in illumination and minor scene changes. The method is evaluated across several different cameras, changes in time-of-day and weather conditions. The results clearly demonstrate the value of the approach.

  • 23.
    Pronobis, Andrzej
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Christensen, Henrik I.
    Caputo, Barbara
    Overview of the ImageCLEF@ICPR 2010 Robot Vision Track2010In: RECOGNIZING PATTERNS IN SIGNALS, SPEECH, IMAGES, AND VIDEOS    / [ed] Unay D; Cataltepe Z; Aksoy S, 2010, Vol. 6388, p. 171-179Conference paper (Refereed)
    Abstract [en]

    This paper describes the robot vision track that has been proposed to the ImageCLEF@ICPR2010 participants. The track addressed the problem of visual place classification. Participants were asked to classify rooms and areas of an office environment on the basis of image sequences captured by a stereo camera mounted on a mobile robot, under varying illumination conditions. The algorithms proposed by the participants had 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 (from different viewpoints and under different conditions), or additional rooms that were not imaged in the training sequence. The participants were asked 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). A total of eight groups participated to the challenge, with 25 runs submitted to the obligatory task, and 5 submitted to the optional task. The best result in the obligatory task was obtained by the Computer Vision and Geometry Laboratory, ETHZ, Switzerland, with, an overall score of 3824.0. The best result in the optional task was obtained by the intelligent Systems and Data Mining Group, University of Castilla-La Mancha, Albacete, Spain, with an overall score of 3881..0.

  • 24.
    Pronobis, Andrzej
    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.
    Fornoni, M.
    Christensen, H. I.
    Caputo, B.
    The robot vision track at ImageCLEF 20102010In: CLEF2010 Working Notes: Working Notes for CLEF 2010 Conference, Padua, Italy, September 22-23, 2010 / [ed] Martin Braschler, Donna Harman, Emanuele Pianta, Nicola Ferro, CEUR-WS , 2010Conference paper (Refereed)
    Abstract [en]

    This paper describes the robot vision track that has been proposed to the ImageCLEF 2010 participants. The track addressed the problem of visual place classification, with a special focus on generalization. Participants were asked to classify rooms and areas of an office environment on the basis of image sequences captured by a stereo camera mounted on a mobile robot, under varying illumination conditions. The algorithms proposed by the participants had to answer the question "where are you?" (I am in the kitchen, in the corridor, etc) when presented with a test sequence, acquired within the same building but at a different oor than the training sequence. The test data contained images of rooms seen during training, or additional rooms that were not imaged in the training sequence. The participants were asked 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). A total of seven groups participated to the challenge, with 42 runs submitted to the obligatory task, and 13 submitted to the optional task. The best result in the obligatory task was obtained by the Computer Vision and Geometry Laboratory, ETHZ, Switzerland, with an overall score of 677. The best result in the optional task was obtained by the Idiap Research Institute, Martigny, Switzerland, with an overall score of 2052.

  • 25.
    Pronobis, Andrzej
    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.
    Hierarchical Multi-modal Place Categorization2011In: Proc. of the European Conference on Mobile Robotics (ECMR'11), 2011Conference paper (Refereed)
    Abstract [en]

    In this paper we present an hierarchical approach to place categorization. Low level sensory data is processed into more abstract concept, named properties of space. The framework allows for fusing information from heterogeneous sensory modalities and a range of derivatives of their data. Place categories are defined based on the properties that decouples them from the low level sensory data. This gives for better scalability, both in terms of memory and computations. The probabilistic inference is performed in a chain graph which supports incremental learning of the room category models. Experimental results are presented where the shape, size and appearance of the rooms are used as properties along with the number of objects of certain classes and the topology of space.

  • 26.
    Pronobis, Andrzej
    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.
    Large-scale semantic mapping and reasoning with heterogeneous modalities2012In: 2012 IEEE International Conference on Robotics and Automation (ICRA), IEEE Computer Society, 2012, p. 3515-3522Conference paper (Refereed)
    Abstract [en]

    This paper presents a probabilistic framework combining heterogeneous, uncertain, information such as object observations, shape, size, appearance of rooms and human input for semantic mapping. It abstracts multi-modal sensory information and integrates it with conceptual common-sense knowledge in a fully probabilistic fashion. It relies on the concept of spatial properties which make the semantic map more descriptive, and the system more scalable and better adapted for human interaction. A probabilistic graphical model, a chaingraph, is used to represent the conceptual information and perform spatial reasoning. Experimental results from online system tests in a large unstructured office environment highlight the system's ability to infer semantic room categories, predict existence of objects and values of other spatial properties as well as reason about unexplored space.

  • 27.
    Pronobis, Andrzej
    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.
    Understanding the Real World: Combining Objects, Appearance, Geometry and Topology for Semantic Mapping2011Report (Other academic)
    Abstract [en]

    A cornerstone for mobile robots operating in man-made environments and interacting with humans is representing and understanding the human semantic concepts of space. In this report, we present a multi-layered semantic mapping algorithm able to combine information about the existence of objects in the environment with knowledge about the topology and semantic properties of space such as room size, shape and general appearance. We use it to infer semantic categories of rooms and predict existence of objects and values of other spatial properties. We perform experiments offline and online on a mobile robot showing the efficiency and usefulness of our system.

  • 28.
    Pronobis, Andrzej
    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.
    Sjöö, Kristoffer
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Zender, Hendrik
    Kruijff, Geert-Jan M.
    Mozos, O. M.
    Burgard, Wolfram
    Semantic modelling of space2010In: Cognitive Systems Monographs: Cognitive Systems / [ed] H. I. Christensen, G.-J. M. Kruijff, J. L. Wyatt, Springer Berlin/Heidelberg, 2010, 8, p. 165-221Chapter in book (Refereed)
    Abstract [en]

    A cornerstone for robotic assistants is their understanding of the space they are to be operating in: an environment built by people for people to live and work in. The research questions we are interested in in this chapter concern spatial understanding, and its connection to acting and interacting in indoor environments. Comparing the way robots typically perceive and represent the world with findings from cognitive psychology about how humans do it, it is evident that there is a large discrepancy. If robots are to understand humans and vice versa, robots need to make use of the same concepts to refer to things and phenomena as a person would do. Bridging the gap between human and robot spatial representations is thus of paramount importance.  A spatial knowledge representation for robotic assistants must address the issues of human-robot communication. However, it must also provide a basis for spatial reasoning and efficient planning. Finally, it must ensure safe and reliable navigation control. Only then can robots be deployed in semi-structured environments, such as offices, where they have to interact with humans in everyday situations.  In order to meet the aforementioned requirements, i.e. robust robot control and human-like conceptualization, in CoSy, we adopted a spatial representation that contains maps at different levels of abstraction. This stepwise abstraction from raw sensory input not only produces maps that are suitable for reliable robot navigation, but also yields a level of representation that is similar to a human conceptualization of spatial organization. Furthermore, this model provides a richer semantic view of an environment that permits the robot to do spatial categorization rather than only instantiation.  This approach is at the heart of the Explorer demonstrator, which is a mobile robot capable of creating a conceptual spatial map of an indoor environment. In the present chapter, we describe how we use multi-modal sensory input provided by a laser range finder and a camera in order to build more and more abstract spatial representations.

  • 29.
    Pronobis, Andrzej
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jie, Luo
    Caputo, Barbara
    The more you learn, the less you store: Memory-controlled incremental SVM for visual place recognition2010In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 28, no 7, p. 1080-1097Article in journal (Refereed)
    Abstract [en]

    The capability to learn from experience is a key property for autonomous cognitive systems working in realistic settings. To this end, this paper presents an SVM-based algorithm, capable of learning model representations incrementally while keeping under control memory requirements. We combine an incremental extension of SVMs [43] with a method reducing the number of support vectors needed to build the decision function without any loss in performance [15] introducing a parameter which permits a user-set trade-off between performance and memory. The resulting algorithm is able to achieve the same recognition results as the original incremental method while reducing the memory growth. Our method is especially suited to work for autonomous systems in realistic settings. We present experiments on two common scenarios in this domain: adaptation in presence of dynamic changes and transfer of knowledge between two different autonomous agents, focusing in both cases on the problem of visual place recognition applied to mobile robot topological localization. Experiments in both scenarios clearly show the power of our approach.

  • 30.
    Pronobis, Andrzej
    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.
    Mozos, O. Martinez
    Caputo, B.
    SVM-based discriminative accumulation scheme for place recognition2008In: 2008 IEEE International Conference On Robotics And Automation, Vols 1-9, 2008, p. 522-529Conference paper (Refereed)
    Abstract [en]

    Integrating information coming from different sensors is a fundamental capabitity for autonomous robots. For complex tasks like topological localization, it would be desirable to use multiple cues, possibly from different modalities, so to achieve robust performance. This paper proposes a new method for integrating multiple cues. For each cue we train a large margin classifier which outputs a set of scores indicating the confidence of the decision. These scores are then used as input to a Support Vector Machine, that learns how to weight each cue, for each class, optimally during training. We call this algorithm SVM-based Discriminative Accumulation Scheme (SVM-DAS). We applied our method to the topological localization task, using vision and laser-based cues. Experimental results clearly show the value of our approach.

  • 31.
    Pronobis, Andrzej
    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.
    Mozos, O. Martinez
    Caputo, B.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Multi-modal Semantic Place Classification2010In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 29, no 2-3, p. 298-320Article in journal (Refereed)
    Abstract [en]

    The ability to represent knowledge about space and its position therein is crucial for a mobile robot. To this end, topological and semantic descriptions are gaining popularity for augmenting purely metric space representations. In this paper we present a multi-modal place classification system that allows a mobile robot to identify places and recognize semantic categories in an indoor environment. The system effectively utilizes information from different robotic sensors by fusing multiple visual cues and laser range data. This is achieved using a high-level cue integration scheme based on a Support Vector Machine (SVM) that learns how to optimally combine and weight each cue. Our multi-modal place classification approach can be used to obtain a real-time semantic space labeling system which integrates information over time and space. We perform an extensive experimental evaluation of the method for two different platforms and environments, on a realistic off-line database and in a live experiment on an autonomous robot. The results clearly demonstrate the effectiveness of our cue integration scheme and its value for robust place classification under varying conditions.

  • 32.
    Pronobis, Andrzej
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. Univ Washington, Paul G Allen Sch Comp Sci & Engn, Seattle, WA 98195 USA..
    Rao, Rajesh P. N.
    Univ Washington, Paul G Allen Sch Comp Sci & Engn, Seattle, WA 98195 USA..
    Learning Deep Generative Spatial Models for Mobile Robots2017In: 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) / [ed] Bicchi, A Okamura, A, IEEE , 2017, p. 755-762Conference paper (Refereed)
    Abstract [en]

    We propose a new probabilistic framework that allows mobile robots to autonomously learn deep, generative models of their environments that span multiple levels of abstraction. Unlike traditional approaches that combine engineered models for low-level features, geometry, and semantics, our approach leverages recent advances in Sum-Product Networks (SPNs) and deep learning to learn a single, universal model of the robot's spatial environment. Our model is fully probabilistic and generative, and represents a joint distribution over spatial information ranging from low-level geometry to semantic interpretations. Once learned, it is capable of solving a wide range of tasks: from semantic classification of places, uncertainty estimation, and novelty detection, to generation of place appearances based on semantic information and prediction of missing data in partial observations. Experiments on laser-range data from a mobile robot show that the proposed universal model obtains performance superior to state-of-the-art models fine-tuned to one specific task, such as Generative Adversarial Networks (GANs) or SVMs.

  • 33.
    Pronobis, Andrzej
    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.
    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.
    Bishop, Adrian N.
    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.
    A Framework for Robust Cognitive Spatial Mapping2009In: 2009 International Conference on Advanced Robotics, ICAR 2009, IEEE , 2009, p. 686-693Conference paper (Refereed)
    Abstract [en]

    Spatial knowledge constitutes a fundamental component of the knowledge base of a cognitive, mobile agent. This paper introduces a rigorously defined framework for building a cognitive spatial map that permits high level reasoning about space along with robust navigation and localization. Our framework builds on the concepts of places and scenes expressed in terms of arbitrary, possibly complex features as well as local spatial relations. The resulting map is topological and discrete, robocentric and specific to the agent's perception. We analyze spatial mapping design mechanics in order to obtain rules for how to define the map components and attempt to prove that if certain design rules are obeyed then certain map properties are guaranteed to be realized. The idea of this paper is to take a step back from existing algorithms and literature and see how a rigorous formal treatment can lead the way towards a powerful spatial representation for localization and navigation. We illustrate the power of our analysis and motivate our cognitive mapping characteristics with some illustrative examples.

  • 34.
    Pronobis, Andrzej
    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.
    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.
    Bishop, Adrian N.
    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.
    Representing spatial knowledge in mobile cognitive systems2010In: Intelligent Autonomous Systems 11, IAS 2010, 2010, p. 133-142Conference paper (Refereed)
    Abstract [en]

    A cornerstone for cognitive mobile agents is to represent the vast body of knowledge about space in which they operate. In order to be robust and efficient, such representation must address requirements imposed on the integrated system as a whole, but also resulting from properties of its components. In this paper, we carefully analyze the problem and design a structure of a spatial knowledge representation for a cognitive mobile system. Our representation is layered and represents knowledge at different levels of abstraction. It deals with complex, crossmodal, spatial knowledge that is inherently uncertain and dynamic. Furthermore, it incorporates discrete symbols that facilitate communication with the user and components of a cognitive system. We present the structure of the representation and propose concrete instantiations.

  • 35.
    Pronobis, Andrzej
    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.
    Xing, Li
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Caputo, Barbara
    Overview of the CLEF 2009 Robot Vision Track2010In: MULTILINGUAL INFORMATION ACCESS EVALUATION II: MULTIMEDIA EXPERIMENTS, PT II / [ed] Peters C; Caputo B; Gonzalez J; Jones GJF; KalpathyCramer J; Muller H; Tsikrika T, 2010, Vol. 6242, p. 110-119Conference paper (Refereed)
    Abstract [en]

    The robot vision track has been proposed to the ImageCLEF participants for the first time in 2009 and attracted considerable attention. The track addressed the problem of visual place recognition applied to robot topological localization. Participants were asked to classify rooms of an office environment on the basis of image sequences captured by a perspective camera mounted on a mobile robot. The algorithms proposed by the participants had 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 participants were asked 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). Robustness of the algorithms was evaluated in presence of variations introduced by changing illumination conditions and dynamic variations observed across a time span of almost two years. The participants submitted 18 runs to the obligatory task, and 9 to the optional task. The best results were obtained by the Idiap Research Institute, Martigny, Switzerland for the obligatory task and the University of Castilla-La Mancha, Albacete, Spain for the optional task.

  • 36.
    Sjöö, Kristoffer
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Andrzej, Pronobis
    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.
    Functional topological relations for qualitative spatial representation2011Conference paper (Refereed)
    Abstract [en]

    In this paper, a framework is proposed for representing knowledge about 3-D space in terms of the functional support and containment relationships, corresponding approximately to the prepositions ``on'' and ``in''. A perceptual model is presented which allows for appraising these qualitative relations given the geometries of objects; also, an axiomatic system for reasoning with the relations is put forward. We implement the system on a mobile robot and show how it can use uncertain visual input to infer a coherent qualitative evaluation of a scene, in terms of these functional relations.

  • 37.
    Sjöö, Kristoffer
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Zender, Hendrik
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Kruijff, Geert-Jan M.
    Pronobis, Andrzej
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Hawes, Nick
    Brenner, Michael
    The explorer system2010In: Cognitive Systems Monographs: Cognitive Systems / [ed] H. I. Christensen, G.-J. M. Kruijff, J. L. Wyatt, Springer Berlin/Heidelberg, 2010, 8, p. 395-421Chapter in book (Refereed)
    Abstract [en]

    In the Explorer scenario we deal with the problems of modeling space, acting in this space and reasoning about it. Spatial models are built using input from sensors such as laser scanners and cameras but equally importantly also based on human input. It is this combination that enables the creation of a spatial model that can support low level tasks such as navigation, as well as interaction. Even combined, the inputs only provide a partial description of the world. By combining this knowledge with a reasoning system and a common sense ontology, further information can be inferred to make the description of the world more complete. Unlike the PlayMate system, all the information that is needed to build the spatial models are not available to it sensors at all times. The Explorer need to move around, i.e. explorer space, to gather information and integrate this into the spatial models. Two main modes for this exploration of space have been investigated within the Explorer scenario. In the first mode the robot explores space together with a user in a home tour fashion. That is, the user shows the robot around their shared environment. This is what we call the Human Augmented Mapping paradigm. The second mode is fully autonomous exploration where the robot moves with the purpose of covering space. In practice the two modes would both be used interchangeably to get the best trade-off between autonomy, shared representation and speed. The focus in the Explorer is not on performing a particular task to perfection, but rather acting within a flexible framework that alleviates the need for scripting and hardwiring. We want to investigate two problems within this context: what information must be exchanged by different parts of the system to make this possible, and how the current state of the world should be represented during such exchanges. One particular interaction which encompasses a lot of the aforementioned issues is giving the robot the ability to talk about space. This interaction raises questions such as:  how can we design models that allow the robot and human to talk about where things are, and how do we link the dialogue and the mapping systems?

  • 38.
    Susano Pinto, André
    et al.
    Dep. Informatics Engineering, Faculty of Engineering, University of Porto, Porto, Portugal.
    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.
    Paulo Reis, Luis
    Dep. Informatics Engineering, Faculty of Engineering, University of Porto, Porto, Portugal.
    Novelty detection using graphical models for semantic room classification2011In: 15th Portuguese Conference on Artificial Intelligence, EPIA 2011, 2011, p. 326-339Conference paper (Refereed)
    Abstract [en]

    This paper presents an approach to the problem of novelty detection in the context of semantic room categorization. The ability to assign semantic labels to areas in the environment is crucial for autonomous agents aiming to perform complex human-like tasks and human interaction. However, in order to be robust and naturally learn the semantics from the human user, the agent must be able to identify gaps in its own knowledge. To this end, we propose a method based on graphical models to identify novel input which does not match any of the previously learnt semantic descriptions. The method employs a novelty threshold defined in terms of conditional and unconditional probabilities. The novelty threshold is then optimized using an unconditional probability density model trained from unlabelled data.

  • 39.
    Ullah, Muhammad Muneeb
    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), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Caputo, B
    Luo, J
    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.
    Christensen, Henrik
    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.
    Towards Robust Place Recognition for Robot Localization2008In: 2008 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-9 / [ed] IEEE, 2008, p. 530-537Conference paper (Refereed)
    Abstract [en]

    Localization and context interpretation are two key competences for mobile robot systems. Visual place recognition, as opposed to purely geometrical models, holds promise of higher flexibility and association of semantics to the model. Ideally, a place recognition algorithm should be robust to dynamic changes and it should perform consistently when recognizing a room (for instance a corridor) in different geographical locations. Also, it should be able to categorize places, a crucial capability for transfer of knowledge and continuous learning. In order to test the suitability of visual recognition algorithms for these tasks, this paper presents a new database, acquired in three different labs across Europe. It contains image sequences of several rooms under dynamic changes, acquired at the same time with a perspective and omnidirectional camera, mounted on a socket. We assess this new database with an appearance based algorithm that combines local features with support vector machines through an ad-hoc kernel. Results show the effectiveness of the approach and the value of the database.

  • 40. Wyatt, Jeremy L.
    et al.
    Aydemir, Alper
    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.
    Brenner, Michael
    Hanheide, Marc
    Hawes, Nick
    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.
    Kristan, Matej
    Kruijff, Geert-Jan M.
    Lison, Pierre
    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), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Vrecko, Alen
    Zender, Hendrik
    Zillich, Michael
    Skocaj, Danijel
    Self-Understanding and Self-Extension: A Systems and Representational Approach2010In: IEEE T AUTON MENT DE, ISSN 1943-0604, Vol. 2, no 4, p. 282-303Article in journal (Refereed)
    Abstract [en]

    There are many different approaches to building a system that can engage in autonomous mental development. In this paper, we present an approach based on what we term self-understanding, by which we mean the explicit representation of and reasoning about what a system does and does not know, and how that knowledge changes under action. We present an architecture and a set of representations used in two robot systems that exhibit a limited degree of autonomous mental development, which we term self-extension. The contributions include: representations of gaps and uncertainty for specific kinds of knowledge, and a goal management and planning system for setting and achieving learning goals.

  • 41.
    Xing, Li
    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. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Multi-cue Discriminative Place Recognition2010In: MULTILINGUAL INFORMATION ACCESS EVALUATION II: MULTIMEDIA EXPERIMENTS, PT II / [ed] Peters C; Caputo B; Gonzalez J; Jones GJF; KalpathyCramer J; Muller H; Tsikrika T, 2010, Vol. 6242, p. 315-323Conference paper (Refereed)
    Abstract [en]

    In this paper we report on our successful participation in the Robot Vision challenge in the ImageCLEF 2009 campaign. We present a place recognition system that employs four different discriminative models trained on different global and local visual cues. In order to provide robust recognition, the outputs generated by the models are combined using a discriminative accumulation method. Moreover, the system is able to provide an indication of the confidence of its decision. We analyse the properties and performance of the system on the training and validation data and report the final score obtained on the test run which ranked first in the obligatory track of the Robot Vision task.

1 - 41 of 41
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