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arXiv · 2210.05984

RING++: Roto-translation Invariant Gram for Global Localization on a Sparse Scan Map

Xuecheng Xu·Sha Lu·Jun Wu·Haojian Lu·Qiuguo Zhu·Yiyi Liao·Rong Xiong·Yue Wang
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Abstract

Global localization plays a critical role in many robot applications. LiDAR-based global localization draws the community's focus with its robustness against illumination and seasonal changes. To further improve the localization under large viewpoint differences, we propose RING++ which has roto-translation invariant representation for place recognition, and global convergence for both rotation and translation estimation. With the theoretical guarantee, RING++ is able to address the large viewpoint difference using a lightweight map with sparse scans. In addition, we derive sufficient conditions of feature extractors for the representation preserving the roto-translation invariance, making RING++ a framework applicable to generic multi-channel features. To the best of our knowledge, this is the first learning-free framework to address all subtasks of global localization in the sparse scan map. Validations on real-world datasets show that our approach demonstrates better performance than state-of-the-art learning-free methods, and competitive performance with learning-based methods. Finally, we integrate RING++ into a multi-robot/session SLAM system, performing its effectiveness in collaborative applications.

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Xuecheng Xu, Sha Lu, Jun Wu, Haojian Lu, Qiuguo Zhu, Yiyi Liao, Rong Xiong, Yue Wang. 2022-10-12. RING++: Roto-translation Invariant Gram for Global Localization on a Sparse Scan Map. https://arxiv.org/abs/2210.05984

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