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 2021. ClusterSLAM: A SLAM backend for simultaneous rigid body clustering and motion estimation. Computational Visual Media 7 , pp. 87-101. 10.1007/s41095-020-0195-3

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (4MB) | 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, their dynamic motions are rarely considered. In this paper, we exploit the consensus of 3D motions for landmarks extracted from the same rigid body for clustering, and to identify static and dynamic objects in a unified manner. Specifically, our algorithm builds a noise-aware motion affinity matrix from landmarks, and uses agglomerative clustering to distinguish rigid bodies. Using decoupled factor graph optimization to revise their shapes and trajectories, 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 simultaneously tracking ego-motion and multiple objects.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Publisher:	Springer
ISSN:	2096-0433
Date of First Compliant Deposit:	19 December 2020
Date of Acceptance:	4 September 2020
Last Modified:	05 May 2023 21:46
URI:	https://orca.cardiff.ac.uk/id/eprint/137134

Citation Data

Cited 7 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item