Keypoint detection and description play a pivotal role in various robotics and autonomous applications including visual odometry (VO), visual navigation, and Simultaneous localization and mapping (SLAM). While a myriad of keypoint detectors and descriptors have been extensively studied in conventional camera images, the effectiveness of these techniques in the context of LiDAR-generated images, i.e. reflectivity and ranges images, has not been assessed. These images have gained attention due to their resilience in adverse conditions such as rain or fog. Additionally, they contain significant textural information that supplements the geometric information provided by LiDAR point clouds in the point cloud registration phase, especially when reliant solely on LiDAR sensors. This addresses the challenge of drift encountered in LiDAR Odometry (LO) within geometrically identical scenarios or where not all the raw point cloud is informative and may even be misleading. This paper aims to analyze the applicability of conventional image key point extractors and descriptors on LiDAR-generated images via a comprehensive quantitative investigation. Moreover, we propose a novel approach to enhance the robustness and reliability of LO. After extracting key points, we proceed to downsample the point cloud, subsequently integrating it into the point cloud registration phase for the purpose of odometry estimation. Our experiment demonstrates that the proposed approach has comparable accuracy but reduced computational overhead, higher odometry publishing rate, and even superior performance in scenarios prone to drift by using the raw point cloud. This, in turn, lays a foundation for subsequent investigations into the integration of LiDAR-generated images with LO. Our code is available on GitHub: https://github.com/TIERS/ws-lidar-as-camera-odom.

翻译：关键点检测与描述在包括视觉里程计（VO）、视觉导航及同步定位与地图构建（SLAM）在内的多种机器人与自主应用中发挥着关键作用。尽管已有大量研究针对传统相机图像中的关键点检测器与描述子进行了深入探讨，但这些技术在激光雷达生成图像（即反射率图像与距离图像）中的有效性尚未得到评估。此类图像因其在雨、雾等恶劣条件下的鲁棒性而备受关注，同时，它们包含丰富的纹理信息，可在点云配准阶段补充激光雷达点云提供的几何信息——尤其当系统仅依赖激光雷达传感器时。这有助于解决激光雷达里程计（LO）在几何同质场景下或原始点云信息不足甚至产生误导时出现的漂移问题。本文旨在通过全面的定量研究，分析传统图像关键点提取器与描述子对激光雷达生成图像的适用性。此外，我们提出一种增强LO鲁棒性与可靠性的新方法。在提取关键点后，我们对点云进行降采样，并将其集成至点云配准阶段以完成里程计估计。实验表明，所提方法在保持相当精度的同时，具有更低的计算开销、更高的里程计发布频率，甚至在易发生漂移的场景中性能优于使用原始点云的方法。这为后续探索激光雷达生成图像与LO的融合奠定了基础。我们的代码已开源至GitHub：https://github.com/TIERS/ws-lidar-as-camera-odom。