基于激光雷达的高效室外定位系统，支持多种传感器且精度超50厘米。
GroundLoc: Efficient Large-Scale Outdoor LiDAR-Only Localization
- 将激光雷达数据投影为鸟瞰图，用R2D2或SIFT提取关键点进行地图匹配。
- 在多个数据集上实现低于50厘米的平均轨迹误差，满足实时运行要求。
- 地图仅需4MB/平方公里，适合大规模户外场景部署，开源可用。
本文提出GroundLoc，一种仅使用激光雷达的定位流水线，可在大规模室外环境中实现移动机器人定位。该方法将感知区域投影为鸟瞰图（BEV），采用R2D2或SIFT算法提取关键点，完成与先验地图的注册。实验表明，GroundLoc在SemanticKITTI和HeLiPR数据集上优于现有方法，多时段定位评估中，所有Ouster OS2 128序列的平均轨迹误差（ATE）均低于50厘米，并满足在线运行需求。系统兼容Velodyne HDL-64E、Ouster OS2 128、Aeva Aeries II及Livox Avia等多种传感器。先验地图以二维栅格图像形式存储，单次采集即可生成，每平方公里仅需4MB存储空间。源代码已公开于https://github.com/dcmlr/groundloc。
原文摘要 · Abstract (English)
In this letter, we introduce GroundLoc, a LiDAR-only localization pipeline designed to localize a mobile robot in large-scale outdoor environments using prior maps. GroundLoc employs a Bird's-Eye View (BEV) image projection focusing on the perceived ground area and utilizes the place recognition network R2D2, or alternatively, the non-learning approach Scale-Invariant Feature Transform (SIFT), to identify and select keypoints for BEV image map registration. Our results demonstrate that GroundLoc outperforms state-of-the-art methods on the SemanticKITTI and HeLiPR datasets across various sensors. In the multi-session localization evaluation, GroundLoc reaches an Average Trajectory Error (ATE) well below 50 cm on all Ouster OS2 128 sequences while meeting online runtime requirements. The system supports various sensor models, as evidenced by evaluations conducted with Velodyne HDL-64E, Ouster OS2 128, Aeva Aeries II, and Livox Avia sensors. The prior maps are stored as 2D raster image maps, which can be created from a single drive and require only 4 MB of storage per square kilometer. The source code is available at https://github.com/dcmlr/groundloc.
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