Characterizing Lidar Range-Measurement Ambiguity due to Multiple Returns
2026-04-10 • Robotics
RoboticsComputer Vision and Pattern Recognition
AI summaryⓘ
The authors study how lidar sensors, which help self-driving cars figure out their position, sometimes give unclear or multiple signals when there are several surfaces in the sensor's view. They point out that these sensors don’t always detect just one clear spot along a beam, which can affect how the car understands its surroundings. By analyzing data from two different rotating lidar devices, the authors show how often and where these uncertain readings happen. They also suggest a way to think about how these mixed signals might impact the car’s navigation accuracy.
lidarpose estimationself-driving carssensor noisemulti-returnraypathcumulative distribution functionmechanically rotating lidarlocalization
Authors
Jason H. Rife, Yifan Li
Abstract
Reliable position and attitude sensing is critical for highly automated vehicles that operate on conventional roadways. Lidar sensors are increasingly incorporated into pose-estimation systems. Despite its great utility, lidar is a complex sensor, and its performance in roadway environments is not yet well understood. For instance, it is often assumed in lidar-localization algorithms that a lidar will always identify a unique surface along a given raypath. However, this assumption is not always true, as ample prior evidence exists to suggest that lidar units may generate measurements probabilistically when more than one scattering surface appears within the lidar's conical beam. In this paper, we analyze lidar datasets to characterize cases with probabilistic returns along particular raypaths. Our contribution is to present representative cumulative distribution functions (CDFs) for raypaths observed by two different mechanically rotating lidar units with stationary bases. In subsequent discussion, we outline a qualitative methodology to assess the effect of probabilistic multi-return cases on lidar-based localization.