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

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