Abstract
Light Detection and Ranging (LiDAR) technology has proven to be an important part of many robotics systems. Surface normals estimated from LiDAR data are commonly used for a variety of tasks in such systems. As most of the today's mechanical LiDAR sensors produce sparse data, estimating normals from a single scan in a robust manner poses difficulties. In this paper, we address the problem of estimating normals for sparse LiDAR data avoiding the typical issues of smoothing out the normals in high curvature areas. Mechanical LiDARs rotate a set of rigidly mounted lasers. One firing of such a set of lasers produces an array of points where each point's neighbor is known due to the known firing pattern of the scanner. We use this knowledge to connect these points to their neighbors and label them using the angles of the lines connecting them. When estimating normals at these points, we only consider points with the same label as neighbors. This allows us to avoid estimating normals in high curvature areas. We evaluate our approach on various data, both self-recorded and publicly available, acquired using various sparse LiDAR sensors. We show that using our method for normal estimation leads to normals that are more robust in areas with high curvature which leads to maps of higher quality. We also show that our method only incurs a constant factor runtime overhead with respect to a lightweight baseline normal estimation procedure and is therefore suited for operation in computationally demanding environments.
Abstract (translated)
光探测和测距（LiDAR）技术已经成为许多机器人系统的重要组成部分。从LiDAR数据中估计的表面法线通常用于这些系统中的各种任务。由于大多数现代机械LiDAR传感器仅产生稀疏数据，在稳健地估计法线方面存在困难。在本文中，我们解决了在稀疏LiDAR数据中估计法线避开高曲率区域典型问题的問題。机械LiDAR将一组刚性安装的激光器旋转。这种设置的每发一次激光器会产生一个点，由于扫描器的已知扫描模式，每个点的邻居已知。我们利用这个知识将这些点连接到邻居并使用它们之间的角度来标记它们。在估计这些点的法线时，我们只考虑具有相同标签的点。这使我们能够避免在高曲率区域中估计法线。我们在各种自记录的和公开可用的数据上评估我们的方法。我们发现，使用我们估计法线的方法可以得到更高曲率区域中的法线，从而实现质量更高的地图。我们还发现，相对于轻量级基线法线估计方法，我们的方法只产生了恒定的运行时开销，因此它适用于计算密集型环境。
URL
https://arxiv.org/abs/2404.14281
