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Robust Intensity-Based Localization Method for Autonomous Driving on Snow-Wet Road Surface
https://doi.org/10.24517/00049793
https://doi.org/10.24517/00049793ddabffa1-5d0d-4018-8def-e5854ca77027
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
FR-PR-ALDIBAJA-M-2369.pdf (1.9 MB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||||||
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| 公開日 | 2018-01-15 | |||||||||
| タイトル | ||||||||||
| タイトル | Robust Intensity-Based Localization Method for Autonomous Driving on Snow-Wet Road Surface | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||
| 資源タイプ | journal article | |||||||||
| ID登録 | ||||||||||
| ID登録 | 10.24517/00049793 | |||||||||
| ID登録タイプ | JaLC | |||||||||
| 著者 |
Aldibaja, Mohammad
× Aldibaja, Mohammad× Suganuma, Naoki× Yoneda, Keisuke |
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| 著者別表示 |
菅沼, 直樹
× 菅沼, 直樹
× 米陀, 佳祐
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| 提供者所属 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | 金沢大学高度モビリティ研究所 | |||||||||
| 書誌情報 |
IEEE Transactions on Industrial Informatics 巻 13, 号 5, p. 2369-2378, 発行日 2017-10 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 1551-3203 | |||||||||
| NCID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AA12023428 | |||||||||
| DOI | ||||||||||
| 関連タイプ | isVersionOf | |||||||||
| 識別子タイプ | DOI | |||||||||
| 関連識別子 | 10.1109/TII.2017.2713836 | |||||||||
| 出版者 | ||||||||||
| 出版者 | IEEE Computer Society | |||||||||
| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | Autonomous vehicles are being developed rapidly in recent years. In advance implementation stages, many particular problems must be solved to bring this technology into the market place. This paper focuses on the problem of driving in snow and wet road surface environments. First, the quality of laser imaging detection and ranging (LIDAR) reflectivity decreases on wet road surfaces. Therefore, an accumulation strategy is designed to increase the density of online LIDAR images. In order to enhance the texture of the accumulated images, principal component analysis is used to understand the geometrical structures and texture patterns in the map images. The LIDAR images are then reconstructed using the leading principal components with respect to the variance distribution accounted by each eigenvector. Second, the appearance of snow lines deforms the expected road context in LIDAR images. Accordingly, the edge profiles of the LIDAR and map images are extracted to encode the lane lines and roadside edges. Edge matching between the two profiles is then calculated to improve localization in the lateral direction. The proposed method has been tested and evaluated using real data that are collected during the winter of 2016-2017 in Suzu and Kanazawa, Japan. The experimental results show that the proposed method increases the robustness of autonomous driving on wet road surfaces, provides a stable performance in laterally localizing the vehicle in the presence of snow lines, and significantly reduces the overall localization error at a speed of 60 km/h. © 2005-2012 IEEE. | |||||||||
| 著者版フラグ | ||||||||||
| 出版タイプ | AM | |||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||
