Characterization of Multiple 3D LiDARs for Localization and Mapping using Normal Distributions Transform
This work provides a benchmark for selecting LiDAR sensors in autonomous driving applications, but it is incremental as it applies an existing method to new data.
The paper compared ten 3D LiDAR sensors for mapping and vehicle localization using the Normal Distributions Transform algorithm, analyzing performance based on metrics like mean map entropy and 6-DOF localization accuracy.
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.