Onboard Sensors-Based Self-Localization for Autonomous Vehicle With Hierarchical Map
| Autor: | Chao Xia, Yanqing Shen, Yuedong Yang, Xiaodong Deng, Shitao Chen, Jingmin Xin, Nanning Zheng |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | IEEE Transactions on Cybernetics. :1-14 |
| ISSN: | 2168-2275 2168-2267 |
| Popis: | Localization is a fundamental and crucial module for autonomous vehicles. Most of the existing localization methodologies, such as signal-dependent methods (RTK-GPS and Bluetooth), simultaneous localization and mapping (SLAM), and map-based methods, have been utilized in outdoor autonomous driving vehicles and indoor robot positioning. However, they suffer from severe limitations, such as signal-blocked scenes of GPS, computing resource occupation explosion in large-scale scenarios, intolerable time delay, and registration divergence of SLAM/map-based methods. In this article, a self-localization framework, without relying on GPS or any other wireless signals, is proposed. We demonstrate that the proposed homogeneous normal distribution transform algorithm and two-way information interaction mechanism could achieve centimeter-level localization accuracy, which reaches the requirement of autonomous vehicle localization for instantaneity and robustness. In addition, benefitting from hardware and software co-design, the proposed localization approach is extremely light-weighted enough to be operated on an embedded computing system, which is different from other LiDAR localization methods relying on high-performance CPU/GPU. Experiments on a public dataset (Baidu Apollo SouthBay dataset) and real-world verified the effectiveness and advantages of our approach compared with other similar algorithms. |
| Databáze: | OpenAIRE |
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