Roadside LiDAR Assisted Cooperative Localization for Connected Autonomous Vehicles
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866909250012315648 |
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| author | Jiang, Yuze Javanmardi, Ehsan Nakazato, Jin Tsukada, Manabu Esaki, Hiroshi |
| author_facet | Jiang, Yuze Javanmardi, Ehsan Nakazato, Jin Tsukada, Manabu Esaki, Hiroshi |
| contents | Advancements in LiDAR technology have led to more cost-effective production while simultaneously improving precision and resolution. As a result, LiDAR has become integral to vehicle localization, achieving centimeter-level accuracy through techniques like Normal Distributions Transform (NDT) and other advanced 3D registration algorithms. Nonetheless, these approaches are reliant on high-definition 3D point cloud maps, the creation of which involves significant expenditure. When such maps are unavailable or lack sufficient features for 3D registration algorithms, localization accuracy diminishes, posing a risk to road safety. To address this, we proposed to use LiDAR-equipped roadside unit and Vehicle-to-Infrastructure (V2I) communication to accurately estimate the connected autonomous vehicle's position and help the vehicle when its self-localization is not accurate enough. Our simulation results indicate that this method outperforms traditional NDT scan matching-based approaches in terms of localization accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_07913 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Roadside LiDAR Assisted Cooperative Localization for Connected Autonomous Vehicles Jiang, Yuze Javanmardi, Ehsan Nakazato, Jin Tsukada, Manabu Esaki, Hiroshi Robotics Advancements in LiDAR technology have led to more cost-effective production while simultaneously improving precision and resolution. As a result, LiDAR has become integral to vehicle localization, achieving centimeter-level accuracy through techniques like Normal Distributions Transform (NDT) and other advanced 3D registration algorithms. Nonetheless, these approaches are reliant on high-definition 3D point cloud maps, the creation of which involves significant expenditure. When such maps are unavailable or lack sufficient features for 3D registration algorithms, localization accuracy diminishes, posing a risk to road safety. To address this, we proposed to use LiDAR-equipped roadside unit and Vehicle-to-Infrastructure (V2I) communication to accurately estimate the connected autonomous vehicle's position and help the vehicle when its self-localization is not accurate enough. Our simulation results indicate that this method outperforms traditional NDT scan matching-based approaches in terms of localization accuracy. |
| title | Roadside LiDAR Assisted Cooperative Localization for Connected Autonomous Vehicles |
| topic | Robotics |
| url | https://arxiv.org/abs/2311.07913 |