使用 SOLiD 缩小 FOV：面向 FOV 受限 LiDAR 地点识别的空间组织轻量级全局描述子
Narrowing your FOV with SOLiD: Spatially Organized and Lightweight Global Descriptor for FOV-constrained LiDAR Place Recognition
AI总结：
针对 FOV 受限导致 LiDAR 地点识别失效的问题，提出轻量级空间组织全局描述子 SOLiD，通过距离/方位-仰角分箱与垂直方向重加权实现旋转鲁棒识别和初始航向估计。
摘要：
在真实机器人导航中，由于传感器融合或传感器安装等多种因素，我们经常会遇到 FOV 受限的情况。然而，有限的 FOV 会中断描述的生成，并对地点识别造成不利影响。因此，在使用 FOV 受限的基于 LiDAR 的地点识别时，我们难以在一致地图中校正累积的漂移误差。为此，本文提出一种鲁棒的基于 LiDAR 的地点识别方法，用于处理狭窄 FOV 场景。所提方法基于距离-仰角 bin 和方位角-仰角 bin 建立空间组织，以表示地点。此外，我们通过基于垂直方向信息的重加权实现鲁棒的地点描述。基于这些表示，我们的方法能够应对旋转变化并确定初始航向。另外，我们为机器人的机载自主性设计了一种轻量且快速的方法。为进行严格验证，所提方法在多种 LiDAR 地点识别场景（即单会话、多会话和多机器人场景）中进行了测试。据我们所知，我们报告了首个应对受限 FOV 的方法。我们的地点描述和 SLAM 代码将会公开。此外，描述子的补充材料可在 https://sites.google.com/view/lidar-solid 获取。
英文摘要：
We often encounter limited FOV situations due to various factors such as sensor fusion or sensor mount in real-world robot navigation. However, the limited FOV interrupts the generation of descriptions and impacts place recognition adversely. Therefore, we suffer from correcting accumulated drift errors in a consistent map using LiDAR-based place recognition with limited FOV. Thus, in this paper, we propose a robust LiDAR-based place recognition method for handling narrow FOV scenarios. The proposed method establishes spatial organization based on the range-elevation bin and azimuth-elevation bin to represent places. In addition, we achieve a robust place description through reweighting based on vertical direction information. Based on these representations, our method enables addressing rotational changes and determining the initial heading. Additionally, we designed a lightweight and fast approach for the robot's onboard autonomy. For rigorous validation, the proposed method was tested across various LiDAR place recognition scenarios (i.e., single-session, multi-session, and multi-robot scenarios). To the best of our knowledge, we report the first method to cope with the restricted FOV. Our place description and SLAM codes will be released. Also, the supplementary materials of our descriptor are available at \texttt{\url{https://sites.google.com/view/lidar-solid}}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
