让点云地图自动带地理坐标并修正长期漂移
FlexCloud: Direct, Modular Georeferencing and Drift-Correction of Point Cloud Maps
- 用3D橡胶板变换技术，仅靠局部点云和里程计实现全局定位
- 无需控制点，直接利用GNSS数据校正地图畸变，保持结构完整
- 兼容多种SLAM系统，适合自动驾驶地图构建场景
当前自动驾驶应用依赖地图信息实现可靠定位、路径规划与运动预测。点云地图生成作为同步定位与地图构建（SLAM）的核心任务，多数最新方法未包含全局位置信息，导致生成的地图存在内部畸变且缺乏地理参照，无法用于基于地图的定位。为此，本文提出FlexCloud，实现由SLAM生成的点云地图的自动地理参照。该方法可模块化适配不同SLAM系统，仅需本地点云地图及其里程计数据，并结合对应GNSS位置，实现无额外控制点的直接地理参照。通过3D橡胶板变换，有效校正因长期漂移造成的地图畸变，同时保持原有结构。本方法可从移动测绘系统（MMS）采集数据中生成一致且全局参考的点云地图。代码开源：https://github.com/TUMFTM/FlexCloud。
原文摘要 · Abstract (English)
Current software stacks for real-world applications of autonomous driving leverage map information to ensure reliable localization, path planning, and motion prediction. An important field of research is the generation of point cloud maps, referring to the topic of simultaneous localization and mapping (SLAM). As most recent developments do not include global position data, the resulting point cloud maps suffer from internal distortion and missing georeferencing, preventing their use for map-based localization approaches. Therefore, we propose FlexCloud for an automatic georeferencing of point cloud maps created from SLAM. Our approach is designed to work modularly with different SLAM methods, utilizing only the generated local point cloud map and its odometry. Using the corresponding GNSS positions enables direct georeferencing without additional control points. By leveraging a 3D rubber-sheet transformation, we can correct distortions within the map caused by long-term drift while maintaining its structure. Our approach enables the creation of consistent, globally referenced point cloud maps from data collected by a mobile mapping system (MMS). The source code of our work is available at https://github.com/TUMFTM/FlexCloud.
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