Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/89905
Citations
Scopus Web of Science® Altmetric
?
?
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
Appears in Collections:Aurora harvest 2
Computer Science publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.