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Publications (10 of 49) Show all publications
Canelhas, D. R., Stoyanov, T. & Lilienthal, A. J. (2018). A Survey of Voxel Interpolation Methods and an Evaluation of Their Impact on Volumetric Map-Based Visual Odometry. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA),: . Paper presented at IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, May 21-25, 2018 (pp. 6337-6343). IEEE Computer Society
Open this publication in new window or tab >>A Survey of Voxel Interpolation Methods and an Evaluation of Their Impact on Volumetric Map-Based Visual Odometry
2018 (English)In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA),, IEEE Computer Society, 2018, p. 6337-6343Conference paper, Published paper (Refereed)
Abstract [en]

Voxel volumes are simple to implement and lend themselves to many of the tools and algorithms available for 2D images. However, the additional dimension of voxels may be costly to manage in memory when mapping large spaces at high resolutions. While lowering the resolution and using interpolation is common work-around, in the literature we often find that authors either use trilinear interpolation or nearest neighbors and rarely any of the intermediate options. This paper presents a survey of geometric interpolation methods for voxel-based map representations. In particular we study the truncated signed distance field (TSDF) and the impact of using fewer than 8 samples to perform interpolation within a depth-camera pose tracking and mapping scenario. We find that lowering the number of samples fetched to perform the interpolation results in performance similar to the commonly used trilinear interpolation method, but leads to higher framerates. We also report that lower bit-depth generally leads to performance degradation, though not as much as may be expected, with voxels containing as few as 3 bits sometimes resulting in adequate estimation of camera trajectories.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Keywords
Voxels, Compression, Interpolation, TSDF, Visual Odometry
National Category
Robotics Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-67850 (URN)000446394504116 ()
Conference
IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, May 21-25, 2018
Projects
H2020 ILIADH2020 Roblog
Funder
EU, Horizon 2020, 732737
Available from: 2018-07-11 Created: 2018-07-11 Last updated: 2018-10-22Bibliographically approved
Stoyanov, T., Krug, R., Kiselev, A., Sun, D. & Loutfi, A. (2018). Assisted Telemanipulation: A Stack-Of-Tasks Approach to Remote Manipulator Control. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 1-5, 2018. IEEE Press
Open this publication in new window or tab >>Assisted Telemanipulation: A Stack-Of-Tasks Approach to Remote Manipulator Control
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2018 (English)In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE Press, 2018Conference paper, Published paper (Refereed)
Abstract [en]

This article presents an approach for assisted teleoperation of a robot arm, formulated within a real-time stack-of-tasks (SoT) whole-body motion control framework. The approach leverages the hierarchical nature of the SoT framework to integrate operator commands with assistive tasks, such as joint limit and obstacle avoidance or automatic gripper alignment. Thereby some aspects of the teleoperation problem are delegated to the controller and carried out autonomously. The key contributions of this work are two-fold: the first is a method for unobtrusive integration of autonomy in a telemanipulation system; and the second is a user study evaluation of the proposed system in the context of teleoperated pick-and-place tasks. The proposed approach of assistive control was found to result in higher grasp success rates and shorter trajectories than achieved through manual control, without incurring additional cognitive load to the operator.

Place, publisher, year, edition, pages
IEEE Press, 2018
Series
IEEE International Conference on Intelligent Robots and Systems. Proceedings, ISSN 2153-0866
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-71310 (URN)10.1109/IROS.2018.8594457 (DOI)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 1-5, 2018
Available from: 2019-01-09 Created: 2019-01-09 Last updated: 2019-01-18Bibliographically approved
Lundell, J., Krug, R., Schaffernicht, E., Stoyanov, T. & Kyrki, V. (2018). Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization. In: IEEE-RAS Conference on Humanoid Robots: . Paper presented at IEEE-RAS 18th Conference on Humanoid Robots (Humanoids 2018), Beijing, China, November 6-9, 2018.
Open this publication in new window or tab >>Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization
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2018 (English)In: IEEE-RAS Conference on Humanoid Robots, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict the consequences of the exploratory actions it takes. Therefore, exploration can lead to collisions with the potential to harm the robot and/or the environment. In this work we address the safety aspect by constraining the exploration to happen in safe-to-explore state spaces. These are formed by decomposing target skills (e.g., grasping) into higher ranked sub-tasks (e.g., collision avoidance, joint limit avoidance) and lower ranked movement tasks (e.g., reaching). Sub-tasks are defined as concurrent controllers (policies) in different operational spaces together with associated Jacobians representing their joint-space mapping. Safety is ensured by only learning policies corresponding to lower ranked sub-tasks in the redundant null space of higher ranked ones. As a side benefit, learning in sub-manifolds of the state-space also facilitates sample efficiency. Reaching skills performed in simulation and grasping skills performed on a real robot validate the usefulness of the proposed approach.

Keywords
Sensorimotor learning, Grasping and Manipulation, Concept and strategy learning
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-71311 (URN)
Conference
IEEE-RAS 18th Conference on Humanoid Robots (Humanoids 2018), Beijing, China, November 6-9, 2018
Available from: 2019-01-09 Created: 2019-01-09 Last updated: 2019-01-22Bibliographically approved
Canelhas, D. R., Schaffernicht, E., Stoyanov, T., Lilienthal, A. & Davison, A. J. (2017). Compressed Voxel-Based Mapping Using Unsupervised Learning. Robotics, 6(3), Article ID 15.
Open this publication in new window or tab >>Compressed Voxel-Based Mapping Using Unsupervised Learning
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2017 (English)In: Robotics, E-ISSN 2218-6581, Vol. 6, no 3, article id 15Article in journal (Refereed) Published
Abstract [en]

In order to deal with the scaling problem of volumetric map representations, we propose spatially local methods for high-ratio compression of 3D maps, represented as truncated signed distance fields. We show that these compressed maps can be used as meaningful descriptors for selective decompression in scenarios relevant to robotic applications. As compression methods, we compare using PCA-derived low-dimensional bases to nonlinear auto-encoder networks. Selecting two application-oriented performance metrics, we evaluate the impact of different compression rates on reconstruction fidelity as well as to the task of map-aided ego-motion estimation. It is demonstrated that lossily reconstructed distance fields used as cost functions for ego-motion estimation can outperform the original maps in challenging scenarios from standard RGB-D (color plus depth) data sets due to the rejection of high-frequency noise content.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI AG, 2017
Keywords
3D mapping, TSDF, compression, dictionary learning, auto-encoder, denoising
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-64420 (URN)10.3390/robotics6030015 (DOI)000419218300002 ()2-s2.0-85030989493 (Scopus ID)
Note

Funding Agencies:

European Commission  FP7-ICT-270350 

H-ICT  732737 

Available from: 2018-01-19 Created: 2018-01-19 Last updated: 2018-01-19Bibliographically approved
Andreasson, H., Adolfsson, D., Stoyanov, T., Magnusson, M. & Lilienthal, A. (2017). Incorporating Ego-motion Uncertainty Estimates in Range Data Registration. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), Vancouver, Canada, September 24–28, 2017 (pp. 1389-1395). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Incorporating Ego-motion Uncertainty Estimates in Range Data Registration
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2017 (English)In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1389-1395Conference paper, Published paper (Refereed)
Abstract [en]

Local scan registration approaches commonlyonly utilize ego-motion estimates (e.g. odometry) as aninitial pose guess in an iterative alignment procedure. Thispaper describes a new method to incorporate ego-motionestimates, including uncertainty, into the objective function of aregistration algorithm. The proposed approach is particularlysuited for feature-poor and self-similar environments,which typically present challenges to current state of theart registration algorithms. Experimental evaluation showssignificant improvements in accuracy when using data acquiredby Automatic Guided Vehicles (AGVs) in industrial productionand warehouse environments.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems, ISSN 2153-0858, E-ISSN 2153-0866
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-62803 (URN)10.1109/IROS.2017.8202318 (DOI)000426978201108 ()2-s2.0-85041958720 (Scopus ID)978-1-5386-2682-5 (ISBN)978-1-5386-2683-2 (ISBN)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), Vancouver, Canada, September 24–28, 2017
Projects
Semantic RobotsILIAD
Funder
Knowledge FoundationEU, Horizon 2020, 732737
Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2018-04-09Bibliographically approved
Ahtiainen, J., Stoyanov, T. & Saarinen, J. (2017). Normal Distributions Transform Traversability Maps: LIDAR-Only Approach for Traversability Mapping in Outdoor Environments. Journal of Field Robotics, 34(3), 600-621
Open this publication in new window or tab >>Normal Distributions Transform Traversability Maps: LIDAR-Only Approach for Traversability Mapping in Outdoor Environments
2017 (English)In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 34, no 3, p. 600-621Article in journal (Refereed) Published
Abstract [en]

Safe and reliable autonomous navigation in unstructured environments remains a challenge for field robots. In particular, operating on vegetated terrain is problematic, because simple purely geometric traversability analysis methods typically classify dense foliage as nontraversable. As traversing through vegetated terrain is often possible and even preferable in some cases (e.g., to avoid executing longer paths), more complex multimodal traversability analysis methods are necessary. In this article, we propose a three-dimensional (3D) traversability mapping algorithm for outdoor environments, able to classify sparsely vegetated areas as traversable, without compromising accuracy on other terrain types. The proposed normal distributions transform traversability mapping (NDT-TM) representation exploits 3D LIDAR sensor data to incrementally expand normal distributions transform occupancy (NDT-OM) maps. In addition to geometrical information, we propose to augment the NDT-OM representation with statistical data of the permeability and reflectivity of each cell. Using these additional features, we train a support-vector machine classifier to discriminate between traversable and nondrivable areas of the NDT-TM maps. We evaluate classifier performance on a set of challenging outdoor environments and note improvements over previous purely geometrical traversability analysis approaches.

Place, publisher, year, edition, pages
John Wiley & Sons, 2017
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-53368 (URN)10.1002/rob.21657 (DOI)000400272700008 ()2-s2.0-84971413791 (Scopus ID)
Note

Funding Agencies:

Finnish Society of Automation  

Finnish Funding Agency for Technology and Innovation (TEKES)  

Forum for Intelligent Machines (FIMA)  

Energy and Life Cycle Cost Efficient Machines (EFFIMA) research program 

Available from: 2016-11-02 Created: 2016-11-02 Last updated: 2018-01-13Bibliographically approved
Canelhas, D. R., Stoyanov, T. & Lilienthal, A. J. (2016). From Feature Detection in Truncated Signed Distance Fields to Sparse Stable Scene Graphs. IEEE Robotics and Automation Letters, 1(2), 1148-1155
Open this publication in new window or tab >>From Feature Detection in Truncated Signed Distance Fields to Sparse Stable Scene Graphs
2016 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, Vol. 1, no 2, p. 1148-1155Article in journal (Refereed) Published
Abstract [en]

With the increased availability of GPUs and multicore CPUs, volumetric map representations are an increasingly viable option for robotic applications. A particularly important representation is the truncated signed distance field (TSDF) that is at the core of recent advances in dense 3D mapping. However, there is relatively little literature exploring the characteristics of 3D feature detection in volumetric representations. In this paper we evaluate the performance of features extracted directly from a 3D TSDF representation. We compare the repeatability of Integral invariant features, specifically designed for volumetric images, to the 3D extensions of Harris and Shi & Tomasi corners. We also study the impact of different methods for obtaining gradients for their computation. We motivate our study with an example application for building sparse stable scene graphs, and present an efficient GPU-parallel algorithm to obtain the graphs, made possible by the combination of TSDF and 3D feature points. Our findings show that while the 3D extensions of 2D corner-detection perform as expected, integral invariants have shortcomings when applied to discrete TSDFs. We conclude with a discussion of the cause for these points of failure that sheds light on possible mitigation strategies.

Place, publisher, year, edition, pages
Piscataway, USA: Institute of Electrical and Electronics Engineers (IEEE), 2016
Keywords
Mapping, recognition
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-53369 (URN)10.1109/LRA.2016.2523555 (DOI)000413726900073 ()2-s2.0-84992291892 (Scopus ID)
Available from: 2016-11-02 Created: 2016-11-02 Last updated: 2018-03-09Bibliographically approved
Stoyanov, T., Krug, R., Muthusamy, R. & Kyrki, V. (2016). Grasp Envelopes: Extracting Constraints on Gripper Postures from Online Reconstructed 3D Models. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016), Daejeong, Korea, October 9-14, 2016 (pp. 885-892). New York: Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Grasp Envelopes: Extracting Constraints on Gripper Postures from Online Reconstructed 3D Models
2016 (English)In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), New York: Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 885-892Conference paper, Published paper (Refereed)
Abstract [en]

Grasping systems that build upon meticulously planned hand postures rely on precise knowledge of object geometry, mass and frictional properties - assumptions which are often violated in practice. In this work, we propose an alternative solution to the problem of grasp acquisition in simple autonomous pick and place scenarios, by utilizing the concept of grasp envelopes: sets of constraints on gripper postures. We propose a fast method for extracting grasp envelopes for objects that fit within a known shape category, placed in an unknown environment. Our approach is based on grasp envelope primitives, which encode knowledge of human grasping strategies. We use environment models, reconstructed from noisy sensor observations, to refine the grasp envelope primitives and extract bounded envelopes of collision-free gripper postures. Also, we evaluate the envelope extraction procedure both in a stand alone fashion, as well as an integrated component of an autonomous picking system.

Place, publisher, year, edition, pages
New York: Institute of Electrical and Electronics Engineers (IEEE), 2016
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-53372 (URN)10.1109/IROS.2016.7759155 (DOI)000391921701009 ()978-1-5090-3762-9 (ISBN)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016), Daejeong, Korea, October 9-14, 2016
Available from: 2016-11-02 Created: 2016-11-02 Last updated: 2018-07-17Bibliographically approved
Stoyanov, T., Vaskevicius, N., Mueller, C. A., Fromm, T., Krug, R., Tincani, V., . . . Echelmeyer, W. (2016). No More Heavy Lifting: Robotic Solutions to the Container-Unloading Problem. IEEE robotics & automation magazine, 23(4), 94-106
Open this publication in new window or tab >>No More Heavy Lifting: Robotic Solutions to the Container-Unloading Problem
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2016 (English)In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 23, no 4, p. 94-106Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE, 2016
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-53371 (URN)10.1109/MRA.2016.2535098 (DOI)000389874400011 ()2-s2.0-84981763797 (Scopus ID)
Note

Funding Agency:

EU FP7 project ROBLOG ICT-270350

Available from: 2016-11-02 Created: 2016-11-02 Last updated: 2018-07-17Bibliographically approved
Krug, R., Stoyanov, T., Tincani, V., Andreasson, H., Mosberger, R., Fantoni, G. & Lilienthal, A. J. (2016). The Next Step in Robot Commissioning: Autonomous Picking and Palletizing. IEEE Robotics and Automation Letters, 1(1), 546-553
Open this publication in new window or tab >>The Next Step in Robot Commissioning: Autonomous Picking and Palletizing
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2016 (English)In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 1, no 1, p. 546-553Article in journal (Refereed) Published
Abstract [en]

So far, autonomous order picking (commissioning) systems have not been able to meet the stringent demands regarding speed, safety, and accuracy of real-world warehouse automation, resulting in reliance on human workers. In this letter, we target the next step in autonomous robot commissioning: automatizing the currently manual order picking procedure. To this end, we investigate the use case of autonomous picking and palletizing with a dedicated research platform and discuss lessons learned during testing in simplified warehouse settings. The main theoretical contribution is a novel grasp representation scheme which allows for redundancy in the gripper pose placement. This redundancy is exploited by a local, prioritized kinematic controller which generates reactive manipulator motions on-the-fly. We validated our grasping approach by means of a large set of experiments, which yielded an average grasp acquisition time of 23.5 s at a success rate of 94.7%. Our system is able to autonomously carry out simple order picking tasks in a humansafe manner, and as such serves as an initial step toward future commercial-scale in-house logistics automation solutions.

Place, publisher, year, edition, pages
Piscataway, USA: Institute of Electrical and Electronics Engineers (IEEE), 2016
Keywords
Logistics, grasping, autonomous vehicle navigation, robot safety, mobile manipulation
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-53370 (URN)10.1109/LRA.2016.2519944 (DOI)000413719900073 ()2-s2.0-84981762372 (Scopus ID)
Funder
EU, FP7, Seventh Framework Programme, ICT-270350Knowledge Foundation, 20140220
Available from: 2016-11-02 Created: 2016-11-02 Last updated: 2018-01-13Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6013-4874

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