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.

翻译：光探测与测距技术已被证明是众多机器人系统的重要组成部分。从激光雷达数据中估计的曲面法线广泛应用于此类系统的各类任务中。由于当今大多数机械式激光雷达传感器产生的数据较为稀疏，如何从单次扫描中鲁棒地估计法线存在困难。本文针对稀疏激光雷达数据的法线估计问题，提出了避免高曲率区域法线平滑化这一典型问题的方法。机械式激光雷达通过旋转一组刚性安装的激光器进行工作。当这样一组激光器发射时，会产生一个点阵列，其中每个点的相邻点可依据扫描仪已知的发射模式确定。我们利用这一知识将这些点与其相邻点连接，并根据连接线的角度对它们进行标记。在估计这些点的法线时，我们仅将具有相同标记的点视为相邻点，从而避免在高曲率区域进行法线估计。我们使用多种稀疏激光雷达传感器采集的数据（包括自记录数据和公开数据）对所提方法进行了评估。结果表明，采用我们的法线估计方法可得到在高曲率区域更鲁棒的法线，进而生成更高质量的映射图。同时，我们的方法相较于轻量级基线法线估计流程仅产生常数级别的运行时开销，因此适用于计算资源受限环境中的操作。