A Spectral Learning Approach to Range-Only SLAM

Byron Boots, Geoff Gordon
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(1):19-26, 2013.

Abstract

We present a novel spectral learning algorithm for simultaneous localization and mapping (SLAM) from range data with known correspondences. This algorithm is an instance of a general spectral system identification framework, from which it inherits several desirable properties, including statistical consistency and no local optima. Compared with popular batch optimization or multiple-hypothesis tracking (MHT) methods for range-only SLAM, our spectral approach offers guaranteed low computational requirements and good tracking performance. Compared with MHT and with popular extended Kalman filter (EKF) or extended information filter (EIF) approaches, our approach does not need to linearize a transition or measurement model. We provide a theoretical analysis of our method, including finite-sample error bounds. Finally, we demonstrate on a real-world robotic SLAM problem that our algorithm is not only theoretically justified, but works well in practice: in a comparison of multiple methods, the lowest errors come from a combination of our algorithm with batch optimization, but our method alone produces nearly as good a result at far lower computational cost.

Cite this Paper

BibTeX
@InProceedings{pmlr-v28-boots13, title = {A Spectral Learning Approach to Range-Only {SLAM}}, author = {Boots, Byron and Gordon, Geoff}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {19--26}, year = {2013}, editor = {Dasgupta, Sanjoy and McAllester, David}, volume = {28}, number = {1}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/boots13.pdf}, url = {https://proceedings.mlr.press/v28/boots13.html}, abstract = {We present a novel spectral learning algorithm for simultaneous localization and mapping (SLAM) from range data with known correspondences. This algorithm is an instance of a general spectral system identification framework, from which it inherits several desirable properties, including statistical consistency and no local optima. Compared with popular batch optimization or multiple-hypothesis tracking (MHT) methods for range-only SLAM, our spectral approach offers guaranteed low computational requirements and good tracking performance. Compared with MHT and with popular extended Kalman filter (EKF) or extended information filter (EIF) approaches, our approach does not need to linearize a transition or measurement model. We provide a theoretical analysis of our method, including finite-sample error bounds. Finally, we demonstrate on a real-world robotic SLAM problem that our algorithm is not only theoretically justified, but works well in practice: in a comparison of multiple methods, the lowest errors come from a combination of our algorithm with batch optimization, but our method alone produces nearly as good a result at far lower computational cost.} }
Endnote
%0 Conference Paper %T A Spectral Learning Approach to Range-Only SLAM %A Byron Boots %A Geoff Gordon %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-boots13 %I PMLR %P 19--26 %U https://proceedings.mlr.press/v28/boots13.html %V 28 %N 1 %X We present a novel spectral learning algorithm for simultaneous localization and mapping (SLAM) from range data with known correspondences. This algorithm is an instance of a general spectral system identification framework, from which it inherits several desirable properties, including statistical consistency and no local optima. Compared with popular batch optimization or multiple-hypothesis tracking (MHT) methods for range-only SLAM, our spectral approach offers guaranteed low computational requirements and good tracking performance. Compared with MHT and with popular extended Kalman filter (EKF) or extended information filter (EIF) approaches, our approach does not need to linearize a transition or measurement model. We provide a theoretical analysis of our method, including finite-sample error bounds. Finally, we demonstrate on a real-world robotic SLAM problem that our algorithm is not only theoretically justified, but works well in practice: in a comparison of multiple methods, the lowest errors come from a combination of our algorithm with batch optimization, but our method alone produces nearly as good a result at far lower computational cost.
RIS
TY - CPAPER TI - A Spectral Learning Approach to Range-Only SLAM AU - Byron Boots AU - Geoff Gordon BT - Proceedings of the 30th International Conference on Machine Learning DA - 2013/02/13 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-boots13 PB - PMLR DP - Proceedings of Machine Learning Research VL - 28 IS - 1 SP - 19 EP - 26 L1 - http://proceedings.mlr.press/v28/boots13.pdf UR - https://proceedings.mlr.press/v28/boots13.html AB - We present a novel spectral learning algorithm for simultaneous localization and mapping (SLAM) from range data with known correspondences. This algorithm is an instance of a general spectral system identification framework, from which it inherits several desirable properties, including statistical consistency and no local optima. Compared with popular batch optimization or multiple-hypothesis tracking (MHT) methods for range-only SLAM, our spectral approach offers guaranteed low computational requirements and good tracking performance. Compared with MHT and with popular extended Kalman filter (EKF) or extended information filter (EIF) approaches, our approach does not need to linearize a transition or measurement model. We provide a theoretical analysis of our method, including finite-sample error bounds. Finally, we demonstrate on a real-world robotic SLAM problem that our algorithm is not only theoretically justified, but works well in practice: in a comparison of multiple methods, the lowest errors come from a combination of our algorithm with batch optimization, but our method alone produces nearly as good a result at far lower computational cost. ER -
APA
Boots, B. & Gordon, G.. (2013). A Spectral Learning Approach to Range-Only SLAM. Proceedings of the 30th International Conference on Machine Learning, in Proceedings of Machine Learning Research 28(1):19-26 Available from https://proceedings.mlr.press/v28/boots13.html.

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