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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
Conference Name: International Conference on Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ (14 Sep 2014 - 18 Sep 2014 : Chicago, USA)
Statement of
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
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Appears in Collections:Aurora harvest 2
Computer Science publications

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