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https://hdl.handle.net/2440/89905
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Type: | Conference paper |
Title: | Hybrid Inference Optimization for robust pose graph estimation |
Author: | Segal, A.V. Reid, I.D. |
Citation: | Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014, pp.2675-2682 |
Publisher: | IEEE |
Issue Date: | 2014 |
Series/Report no.: | IEEE International Conference on Intelligent Robots and Systems |
ISBN: | 9781479969340 |
ISSN: | 2153-0858 2153-0866 |
Conference Name: | International Conference on Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ (14 Sep 2014 - 18 Sep 2014 : Chicago, USA) |
Statement of Responsibility: | Aleksandr V. Segal and Ian D. Reid |
Abstract: | In this paper we introduce a new optimization algorithm for networks of switched nonlinear objectives and apply this to the important problem of pose graph estimation for robot localization and mapping. The key insight is to replace the linear solver typically used in Gauss-Newton style methods with hybrid inference over switched discrete/continuous linear Gaussian networks. Since exact inference in these networks is known to be NP-hard, we also propose an approximate inference algorithm for the linearized hybrid networks based on message passing. We apply the new algorithm to the problem of robust pose graph estimation in the presence of incorrect loop closures and compare against three recently published approaches to the same problem. Evaluation is performed on ten sequences from two different datasets and shows that our approach performs substantially better than the state of the art. |
Rights: | ©2014 IEEE |
DOI: | 10.1109/IROS.2014.6942928 |
Grant ID: | http://purl.org/au-research/grants/arc/DP130104413 http://purl.org/au-research/grants/arc/FL130100102 http://purl.org/au-research/grants/arc/CE140100016 |
Published version: | http://dx.doi.org/10.1109/iros.2014.6942928 |
Appears in Collections: | Aurora harvest 2 Computer Science publications |
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