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

Huang, Jiahui, Yang, Sheng, Zhao, Zishuo, Lai, Yukun and Hu, Shi-Min 2020. 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

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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: Published Online
Status: Published
Schools: Computer Science & Informatics
Publisher: IEEE
ISBN: 978-1-7281-4804-5
Related URLs:
Date of First Compliant Deposit: 19 August 2019
Date of Acceptance: 22 July 2019
Last Modified: 26 Jul 2021 17:03

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