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<p>Solid-state Light Detection and Ranging (LiDAR) provides an accurate, robust, cost-effective, and lightweight localization solution for autonomous systems across aerial and ground domains operating in GNSS-challenging and GNSS-denied environments. With a pre-built map of landmarks, autonomous systems can estimate their positions using real-time LiDAR scans, a process commonly known as re-localization. However, LiDAR scans acquired in complex and dynamic environments may contain outliers, which could lead to hazardous localization errors and threaten system safety. Therefore, this paper develops an efficient Fault Detection and Exclusion (FDE) approach for solid-state LiDAR re-localization by employing Multiple Hypothesis Solution Separation (MHSS). We first analyze the fault sources for LiDAR re-localization and then develop an FDE approach to detect and exclude outliers from LiDAR scans in real time. In addition, a point cloud clustering strategy is introduced to reduce the computation complexity. Experiment results show that the proposed FDE approach mitigates the effect of outliers and enhances localization reliability.</p>

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Solution separation-based fault detection and exclusion for reliable solid-state LiDAR localization

  • 期刊论文

  • 发表日期：

    2026年4月15日

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  • Tianqi Xu,
  • Shizhuang Wang,
  • Jiahui Liu,
  • Chenzhang Ning,
  • Yawei Zhai,
  • Xingqun Zhan

摘要

Solid-state Light Detection and Ranging (LiDAR) provides an accurate, robust, cost-effective, and lightweight localization solution for autonomous systems across aerial and ground domains operating in GNSS-challenging and GNSS-denied environments. With a pre-built map of landmarks, autonomous systems can estimate their positions using real-time LiDAR scans, a process commonly known as re-localization. However, LiDAR scans acquired in complex and dynamic environments may contain outliers, which could lead to hazardous localization errors and threaten system safety. Therefore, this paper develops an efficient Fault Detection and Exclusion (FDE) approach for solid-state LiDAR re-localization by employing Multiple Hypothesis Solution Separation (MHSS). We first analyze the fault sources for LiDAR re-localization and then develop an FDE approach to detect and exclude outliers from LiDAR scans in real time. In addition, a point cloud clustering strategy is introduced to reduce the computation complexity. Experiment results show that the proposed FDE approach mitigates the effect of outliers and enhances localization reliability.