Characterizing Lidar Range-Measurement Ambiguity due to Multiple Returns

TL;DR

This paper analyzes lidar measurement ambiguity caused by multiple returns, presenting CDFs for different lidar units and discussing implications for localization accuracy in automated vehicles.

Contribution

It introduces a methodology and provides empirical CDF data to characterize probabilistic multi-return cases in lidar measurements.

Findings

CDFs for two lidar units' multi-return cases are presented.

Probabilistic returns can affect lidar-based localization accuracy.

A qualitative assessment methodology for multi-return effects is outlined.

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.