Fast and Robust Normal Estimation for Sparse LiDAR Scans

TL;DR

This paper presents a novel method for estimating surface normals from sparse LiDAR scans that improves robustness in high curvature areas while maintaining computational efficiency, enhancing map quality for robotics applications.

Contribution

The proposed approach leverages the known firing pattern of mechanical LiDARs to connect points and label neighbors, avoiding smoothing in high curvature regions, which is a novel technique for normal estimation.

Findings

Normals are more robust in high curvature areas.

Method incurs only constant overhead compared to baseline.

Results show improved map quality from better normal estimation.

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.