Characterization of Multiple 3D LiDARs for Localization and Mapping Performance using the NDT Algorithm

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Characterization of Multiple 3D LiDARs for Localization and Mapping Performance using the NDT Algorithm

Carballo, Alexander / Monrroy, Abraham / Wong, David / Narksri, Patiphon / Lambert, Jacob / Kitsukawa, Yuki / Takeuchi, Eijiro / Kato, Shinpei / Takeda, Kazuya

Abstract

In this work, we present a detailed comparison of ten different 3D LiDAR sensors 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.

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Dokumentinformationen

Titel :

Characterization of Multiple 3D LiDARs for Localization and Mapping Performance using the NDT Algorithm

Beteiligte:

Carballo, Alexander (Autor:in) / Monrroy, Abraham (Autor:in) / Wong, David (Autor:in) / Narksri, Patiphon (Autor:in) / Lambert, Jacob (Autor:in) / Kitsukawa, Yuki (Autor:in) / Takeuchi, Eijiro (Autor:in) / Kato, Shinpei (Autor:in) / Takeda, Kazuya (Autor:in)

Erschienen in:

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) ; 327-334

Erscheinungsdatum :

2021-07-11

Format / Umfang :

3328869 byte

ISBN :

978-1-6654-7921-9

DOI :

https://doi.org/10.1109/IVWorkshops54471.2021.9669244

Medientyp :

Aufsatz (Konferenz)