Change search
Refine search result
1 - 14 of 14
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    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.
    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.

  • 2.
    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.

  • 3.
    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.

  • 4.
    Bore, Nils
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Detection and Tracking of General Movable Objects in Large 3D MapsManuscript (preprint) (Other academic)
    Abstract [en]

    This paper studies the problem of detection and tracking of general objects with long-term dynamics, observed by a mobile robot moving in a large environment. A key problem is that due to the environment scale, it can only observe a subset of the objects at any given time. Since some time passes between observations of objects in different places, the objects might be moved when the robot is not there. We propose a model for this movement in which the objects typically only move locally, but with some small probability they jump longer distances, through what we call global motion. For filtering, we decompose the posterior over local and global movements into two linked processes. The posterior over the global movements and measurement associations is sampled, while we track the local movement analytically using Kalman filters. This novel filter is evaluated on point cloud data gathered autonomously by a mobile robot over an extended period of time. We show that tracking jumping objects is feasible, and that the proposed probabilistic treatment outperforms previous methods when applied to real world data. The key to efficient probabilistic tracking in this scenario is focused sampling of the object posteriors.

  • 5.
    Bore, Nils
    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.
    Multiple Object Detection, Tracking and Long-Term Dynamics Learning in Large 3D MapsManuscript (preprint) (Other academic)
    Abstract [en]

    In this work, we present a method for tracking and learning the dynamics of all objects in a large scale robot environment. A mobile robot patrols the environment and visits the different locations one by one. Movable objects are discovered by change detection, and tracked throughout the robot deployment. For tracking, we extend our previous Rao-Blackwellized particle filter with birth and death processes, enabling the method to handle an arbitrary number of objects. Target births and associations are sampled using Gibbs sampling. The parameters of the system are then learnt using the Expectation Maximization algorithm in an unsupervised fashion. The system therefore enables learning of the dynamics of one particular environment, and of its objects. The algorithm is evaluated on data collected autonomously by a mobile robot in an office environment during a real-world deployment. We show that the algorithm automatically identifies and tracks the moving objects within 3D maps and infers plausible dynamics models, significantly decreasing the modeling bias of our previous work. The proposed method represents an improvement over previous methods for environment dynamics learning as it allows for learning of fine grained processes.

  • 6.
    Bore, Nils
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Object Instance Detection and Dynamics Modeling in a Long-Term Mobile Robot Context2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In the last years, simple service robots such as autonomous vacuum cleaners and lawn mowers have become commercially available and increasingly common. The next generation of service robots should perform more advanced tasks, such as to clean up objects. Robots then need to learn to robustly navigate, and manipulate, cluttered environments, such as an untidy living room. In this thesis, we focus on representations for tasks such as general cleaning and fetching of objects. We discuss requirements for these specific tasks, and argue that solving them would be generally useful, because of their object-centric nature. We rely on two fundamental insights in our approach to understand environments on a fine-grained level. First, many of today's robot map representations are limited to the spatial domain, and ignore that there is a time axis that constrains how much an environment may change during a given period. We argue that it is of critical importance to also consider the temporal domain. By studying the motion of individual objects, we can enable tasks such as general cleaning and object fetching. The second insight comes from that mobile robots are becoming more robust. They can therefore collect large amounts of data from those environments. With more data, unsupervised learning of models becomes feasible, allowing the robot to adapt to changes in the environment, and to scenarios that the designer could not foresee. We view these capabilities as vital for robots to become truly autonomous. The combination of unsupervised learning and dynamics modelling creates an interesting symbiosis: the dynamics vary between different environments and between the objects in one environment, and learning can capture these variations. A major difficulty when modeling environment dynamics is that the whole environment can not be observed at one time, since the robot is moving between different places. We demonstrate how this can be dealt with in a principled manner, by modeling several modes of object movement. We also demonstrate methods for detection and learning of objects and structures in the static parts of the maps. Using the complete system, we can represent and learn many aspects of the full environment. In real-world experiments, we demonstrate that our system can keep track of varied objects in large and highly dynamic environments.​

  • 7.
    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.

  • 8.
    Bore, Nils
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Ekekrantz, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Jensfelt, Patric
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Folkesson, John
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Detection and Tracking of General Movable Objects in Large Three-Dimensional Maps2019In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 35, no 1, p. 231-247Article in journal (Refereed)
    Abstract [en]

    This paper studies the problem of detection and tracking of general objects with semistatic dynamics observed by a mobile robot moving in a large environment. A key problem is that due to the environment scale, the robot can only observe a subset of the objects at any given time. Since some time passes between observations of objects in different places, the objects might be moved when the robot is not there. We propose a model for this movement in which the objects typically only move locally, but with some small probability they jump longer distances through what we call global motion. For filtering, we decompose the posterior over local and global movements into two linked processes. The posterior over the global movements and measurement associations is sampled, while we track the local movement analytically using Kalman filters. This novel filter is evaluated on point cloud data gathered autonomously by a mobile robot over an extended period of time. We show that tracking jumping objects is feasible, and that the proposed probabilistic treatment outperforms previous methods when applied to real world data. The key to efficient probabilistic tracking in this scenario is focused sampling of the object posteriors.

  • 9.
    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.

  • 10.
    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.

  • 11.
    Bore, Nils
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Torroba, Ignacio
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Folkesson, John
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Sparse Gaussian Process SLAM, Storage and Filtering for AUV Multibeam Bathymetry2018In: 2018 IEEE OES Autonomous Underwater Vehicle Symposium, 2018Conference paper (Refereed)
    Abstract [en]

    With dead-reckoning from velocity sensors,AUVs may construct short-term, local bathymetry mapsof the sea floor using multibeam sensors. However, theposition estimate from dead-reckoning will include somedrift that grows with time. In this work, we focus on long-term onboard storage of these local bathymetry maps,and the alignment of maps with respect to each other. Wepropose using Sparse Gaussian Processes for this purpose,and show that the representation has several advantages,including an intuitive alignment optimization, data com-pression, and sensor noise filtering. We demonstrate thesethree key capabilities on two real-world datasets.

  • 12. Hawes, N
    et al.
    Ambrus, Rares
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Bore, Nils
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Folkesson, John
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Hanheide, Marc
    et al.,
    The STRANDS Project Long-Term Autonomy in Everyday Environments2017In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 24, no 3, p. 146-156Article in journal (Refereed)
  • 13.
    Karaoguz, Hakan
    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.
    Human-Centric Partitioning of the Environment2017In: 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), IEEE, 2017, p. 844-850Conference paper (Refereed)
    Abstract [en]

    In this paper, we present an object based approach for human-centric partitioning of the environment. Our approach for determining the human-centric regionsis to detect the objects that are commonly associated withfrequent human presence. In order to detect these objects, we employ state of the art perception techniques. The detected objects are stored with their spatio-temporal information inthe robot’s memory to be later used for generating the regions.The advantages of our method is that it is autonomous, requires only a small set of perceptual data and does not even require people to be present while generating the regions.The generated regions are validated using a 1-month dataset collected in an indoor office environment. The experimental results show that although a small set of perceptual data isused, the regions are generated at densely occupied locations.

  • 14.
    Torroba, Ignacio
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Bore, Nils
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Folkesson, John
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    A Comparison of Submaps Registration Methods for Multibeam Bathymetric Mapping2018Conference paper (Refereed)
    Abstract [en]

    On-the-fly registration of overlapping multi-beam images is important for path planning by AUVs per-forming underwater surveys. In order to meet specificationon such things as survey accuracy, coverage and density,precise corrections to the AUV trajectory while underwayare required. There are fast methods for aligning pointclouds that have been developed for robots. We compareseveral state of the art methods to align point clouds oflarge, unstructured, sub-aquatic areas to build a globalmap. We first collect the multibeam point clouds intosmaller submaps that are then aligned using variationsof the ICP algorithm. This alignment step can be appliedif the error in AUV pose is small. It would be the finalstep in correcting a larger error on loop closing where aplace recognition and a rough alignment would precedeit. In the case of a lawn mower pattern survey it would bemaking more continuous corrections to small errors in theoverlap between parallel lines. In this work we comparedifferent methods for registration in order to determinethe most suitable one for underwater terrain mapping. Todo so, we benchmark the current state of the art solutionsaccording to an error metrics and show the results.

1 - 14 of 14
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf