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A Survey on Global LiDAR Localization: Challenges, Advances and Open Problems

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Abstract

Knowledge about the own pose is key for all mobile robot applications. Thus pose estimation is part of the core functionalities of mobile robots. Over the last two decades, LiDAR scanners have become the standard sensor for robot localization and mapping. This article aims to provide an overview of recent progress and advancements in LiDAR-based global localization. We begin by formulating the problem and exploring the application scope. We then present a review of the methodology, including recent advancements in several topics, such as maps, descriptor extraction, and cross-robot localization. The contents of the article are organized under three themes. The first theme concerns the combination of global place retrieval and local pose estimation. The second theme is upgrading single-shot measurements to sequential ones for sequential global localization. Finally, the third theme focuses on extending single-robot global localization to cross-robot localization in multi-robot systems. We conclude the survey with a discussion of open challenges and promising directions in global LiDAR localization. To our best knowledge, this is the first comprehensive survey on global LiDAR localization for mobile robots.

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(Source: LPDNet (Liu et al., 2019), used with permission.)

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(Source: adapted from G3Reg (Qiao et al., 2023), used with permission.)

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(Source: DiSCO (Xuecheng et al., 2021), used with permission.)

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(Source: SegMatch (Dubé et al., 2017), used with permission.) (Color figure online)

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Acknowledgements

We would like to thank Dr. Xiaqing Ding for her constructive suggestions.

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Authors and Affiliations

  1. Hong Kong University of Science and Technology, Hong Kong SAR, China

    Huan Yin & Shaojie Shen

  2. Zhejiang University, Hangzhou, China

    Xuecheng Xu, Sha Lu, Rong Xiong & Yue Wang

  3. National University of Defense Technology, Changsha, China

    Xieyuanli Chen

  4. University of Bonn, Bonn, Germany

    Cyrill Stachniss

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This work was supported in part by the National Nature Science Foundation of China under Grant 62373322, in part by the HKUST-DJI Joint Innovation Laboratory, and in part by the Hong Kong Center for Construction Robotics (InnoHK center supported by Hong Kong ITC).

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Yin, H., Xu, X., Lu, S. et al. A Survey on Global LiDAR Localization: Challenges, Advances and Open Problems. Int J Comput Vis (2024). https://doi.org/10.1007/s11263-024-02019-5

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