Characterization of Multiple 3D LiDARs for Localization and Mapping using Normal Distributions Transform

TL;DR

This paper compares ten different 3D LiDAR sensors for mapping and localization tasks using the NDT algorithm, providing insights into their performance and characteristics based on a comprehensive urban dataset.

Contribution

It offers a detailed evaluation of multiple LiDAR sensors for mapping and localization using a standardized NDT-based framework and a new urban LiDAR dataset.

Findings

Performance varies significantly across sensors for mapping quality.

Certain sensors achieve higher localization accuracy in urban environments.

Map entropy correlates with sensor resolution and data quality.

Abstract

In this work, we present a detailed comparison of ten different 3D LiDAR sensors, covering a range of manufacturers, models, and laser configurations, for the tasks of mapping and vehicle localization, using as common reference the Normal Distributions Transform (NDT) algorithm implemented in the self-driving open source platform Autoware. LiDAR data used in this study is a subset of our LiDAR Benchmarking and Reference (LIBRE) dataset, captured independently from each sensor, from a vehicle driven on public urban roads multiple times, at different times of the day. In this study, we analyze the performance and characteristics of each LiDAR for the tasks of (1) 3D mapping including an assessment map quality based on mean map entropy, and (2) 6-DOF localization using a ground truth reference map.