In LiDAR sensing, glass, mirrors and other material often cause inconsistent data readings, because the laser beams may report the distance of the glass, the distance of the object behind the glass or the distance to a reflected object. This causes problems in robotics and 3D reconstruction, especially with respect to localization, mapping and thus navigation. With dual-return LiDARs and other methods, one can detect the glass plane and classify the points in a single scan. In this work we go one step further and construct a global, optimized map of reflective planes, in order to then classify all LiDAR readings at the end. As our experiments will show, this approach provides superior classification accuracy compared to the single scan approach. The code and data for this work are available as open source online.

翻译：在激光雷达（LiDAR）传感中，玻璃、镜面及其他材质常导致数据读数不一致，因为激光束可能返回玻璃本身的距离、玻璃后方物体的距离或反射物体的距离。这给机器人技术与三维重建领域带来问题，尤其在定位、建图及由此衍生的导航任务中。借助双回波激光雷达及其他方法，可在单次扫描中检测玻璃平面并对点云进行分类。本研究在此基础上进一步构建全局优化的反射面地图，从而最终对所有激光雷达读数进行分类。实验结果表明，相较于单次扫描方法，本方法能提供更优越的分类精度。本研究的代码与数据已作为开源资源在线发布。