ClusterSLAM: A SLAM backend for simultaneous rigid body clustering and motion estimation

Huang, Jiahui, Yang, Sheng, Zhao, Zishuo, Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680 and Hu, Shi-Min ORCID: https://orcid.org/0000-0001-7507-6542 2019. ClusterSLAM: A SLAM backend for simultaneous rigid body clustering and motion estimation. Presented at: IEEE International Conference on Computer Vision (ICCV), Seoul, Korea, 27 October - 2 November 2019. 2019 IEEE/CVF International Conference on Computer Vision (ICCV) Proceedings. IEEE, 10.1109/ICCV.2019.00597

Preview

PDF - Accepted Post-Print Version Download (1MB) | Preview

Abstract

We present a practical backend for stereo visual SLAM which can simultaneously discover individual rigid bodies and compute their motions in dynamic environments. While recent factor graph based state optimization algorithms have shown their ability to robustly solve SLAM problems by treating dynamic objects as outliers, the dynamic motions are rarely considered. In this paper, we exploit the consensus of 3D motions among the landmarks extracted from the same rigid body for clustering and estimating static and dynamic objects in a unified manner. Specifically, our algorithm builds a noise-aware motion affinity matrix upon landmarks, and uses agglomerative clustering for distinguishing those rigid bodies. Accompanied by a decoupled factor graph optimization for revising their shape and trajectory, we obtain an iterative scheme to update both cluster assignments and motion estimation reciprocally. Evaluations on both synthetic scenes and KITTI demonstrate the capability of our approach, and further experiments considering online efficiency also show the effectiveness of our method for simultaneous tracking of ego-motion and multiple objects.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	9781728148045
Related URLs:	Organisation
Date of First Compliant Deposit:	19 August 2019
Date of Acceptance:	22 July 2019
Last Modified:	07 Dec 2022 13:35
URI:	https://orca.cardiff.ac.uk/id/eprint/125005

Citation Data

Cited 21 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item