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Publications (6 of 6) Show all publications
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
Canelhas, D. R. (2017). Truncated Signed Distance Fields Applied To Robotics. (Doctoral dissertation). Örebro: Örebro University
Open this publication in new window or tab >>Truncated Signed Distance Fields Applied To Robotics
2017 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

This thesis is concerned with topics related to dense mapping of large scale three-dimensional spaces. In particular, the motivating scenario of this work is one in which a mobile robot with limited computational resources explores an unknown environment using a depth-camera. To this end, low-level topics such as sensor noise, map representation, interpolation, bit-rates, compression are investigated, and their impacts on more complex tasks, such as feature detection and description, camera-tracking, and mapping are evaluated thoroughly. A central idea of this thesis is the use of truncated signed distance fields (TSDF) as a map representation and a comprehensive yet accessible treatise on this subject is the first major contribution of this dissertation. The TSDF is a voxel-based representation of 3D space that enables dense mapping with high surface quality and robustness to sensor noise, making it a good candidate for use in grasping, manipulation and collision avoidance scenarios.

The second main contribution of this thesis deals with the way in which information can be efficiently encoded in TSDF maps. The redundant way in which voxels represent continuous surfaces and empty space is one of the main impediments to applying TSDF representations to large-scale mapping. This thesis proposes two algorithms for enabling large-scale 3D tracking and mapping: a fast on-the-fly compression method based on unsupervised learning, and a parallel algorithm for lifting a sparse scene-graph representation from the dense 3D map.

The third major contribution of this work consists of thorough evaluations of the impacts of low-level choices on higher-level tasks. Examples of these are the relationships between gradient estimation methods and feature detector repeatability, voxel bit-rate, interpolation strategy and compression ratio on camera tracking performance. Each evaluation thus leads to a better understanding of the trade-offs involved, which translate to direct recommendations for future applications, depending on their particular resource constraints.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2017. p. 161
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 76
Keywords
3D mapping, pose estimation, feature detection, shape description, compression, unsupervised learning
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-59369 (URN)978-91-7529-209-0 (ISBN)
Public defence
2017-10-13, Örebro universitet, Långhuset, Hörsal L2, Örebro, 13:15 (English)
Opponent
Supervisors
Available from: 2017-08-25 Created: 2017-08-25 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., 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
Canelhas, D. R., Stoyanov, T. & Lilienthal, A. J. (2013). Improved local shape feature stability through dense model tracking. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 3-7, 2013. Tokyo, Japan (pp. 3203-3209). IEEE
Open this publication in new window or tab >>Improved local shape feature stability through dense model tracking
2013 (English)In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2013, p. 3203-3209Conference paper, Published paper (Refereed)
Abstract [en]

In this work we propose a method to effectively remove noise from depth images obtained with a commodity structured light sensor. The proposed approach fuses data into a consistent frame of reference over time, thus utilizing prior depth measurements and viewpoint information in the noise removal process. The effectiveness of the approach is compared to two state of the art, single-frame denoising methods in the context of feature descriptor matching and keypoint detection stability. To make more general statements about the effect of noise removal in these applications, we extend a method for evaluating local image gradient feature descriptors to the domain of 3D shape descriptors. We perform a comparative study of three classes of such descriptors: Normal Aligned Radial Features, Fast Point Feature Histograms and Depth Kernel Descriptors; and evaluate their performance on a real-world industrial application data set. We demonstrate that noise removal enabled by the dense map representation results in major improvements in matching across all classes of descriptors as well as having a substantial positive impact on keypoint detection reliability

Place, publisher, year, edition, pages
IEEE, 2013
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-30524 (URN)10.1109/IROS.2013.6696811 (DOI)000331367403040 ()2-s2.0-84893746421 (Scopus ID)978-1-4673-6358-7 (ISBN)
Conference
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 3-7, 2013. Tokyo, Japan
Available from: 2013-08-30 Created: 2013-08-30 Last updated: 2018-01-11Bibliographically approved
Canelhas, D. R., Stoyanov, T. & Lilienthal, A. J. (2013). SDF tracker: a parallel algorithm for on-line pose estimation and scene reconstruction from depth images. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),November 3-7, 2013. Tokyo, Japan (pp. 3671-3676). IEEE
Open this publication in new window or tab >>SDF tracker: a parallel algorithm for on-line pose estimation and scene reconstruction from depth images
2013 (English)In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2013, p. 3671-3676Conference paper, Published paper (Refereed)
Abstract [en]

Ego-motion estimation and environment mapping are two recurring problems in the field of robotics. In this work we propose a simple on-line method for tracking the pose of a depth camera in six degrees of freedom and simultaneously maintaining an updated 3D map, represented as a truncated signed distance function. The distance function representation implicitly encodes surfaces in 3D-space and is used directly to define a cost function for accurate registration of new data. The proposed algorithm is highly parallel and achieves good accuracy compared to state of the art methods. It is suitable for reconstructing single household items, workspace environments and small rooms at near real-time rates, making it practical for use on modern CPU hardware

Place, publisher, year, edition, pages
IEEE, 2013
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-30523 (URN)10.1109/IROS.2013.6696880 (DOI)000331367403108 ()2-s2.0-84893790149 (Scopus ID)978-1-4673-6358-7 (ISBN)
Conference
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),November 3-7, 2013. Tokyo, Japan
Available from: 2013-08-30 Created: 2013-08-30 Last updated: 2018-01-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7035-5710

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