Zhang Yao, Chu Liang, Mao Yabin, Yu Xintong, Wang Jiawei, Guo Chong
National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China.
China FAW Group Co., Ltd., Changchun 130000, China.
Sensors (Basel). 2024 May 12;24(10):3079. doi: 10.3390/s24103079.
This paper presents an enhanced ground vehicle localization method designed to address the challenges associated with state estimation for autonomous vehicles operating in diverse environments. The focus is specifically on the precise localization of position and orientation in both local and global coordinate systems. The proposed approach integrates local estimates generated by existing visual-inertial odometry (VIO) methods into global position information obtained from the Global Navigation Satellite System (GNSS). This integration is achieved through optimizing fusion in a pose graph, ensuring precise local estimation and drift-free global position estimation. Considering the inherent complexities in autonomous driving scenarios, such as the potential failures of a visual-inertial navigation system (VINS) and restrictions on GNSS signals in urban canyons, leading to disruptions in localization outcomes, we introduce an adaptive fusion mechanism. This mechanism allows seamless switching between three modes: utilizing only VINS, using only GNSS, and normal fusion. The effectiveness of the proposed algorithm is demonstrated through rigorous testing in the Carla simulation environment and challenging UrbanNav scenarios. The evaluation includes both qualitative and quantitative analyses, revealing that the method exhibits robustness and accuracy.
本文提出了一种增强型地面车辆定位方法，旨在应对在不同环境中运行的自动驾驶车辆状态估计所面临的挑战。重点特别在于在局部和全局坐标系中精确确定位置和方向。所提出的方法将现有视觉惯性里程计（VIO）方法生成的局部估计与从全球导航卫星系统（GNSS）获得的全局位置信息进行整合。这种整合是通过在姿态图中优化融合来实现的，确保精确的局部估计和无漂移的全局位置估计。考虑到自动驾驶场景中固有的复杂性，例如视觉惯性导航系统（VINS）的潜在故障以及城市峡谷中GNSS信号的限制，导致定位结果中断，我们引入了一种自适应融合机制。该机制允许在三种模式之间无缝切换：仅使用VINS、仅使用GNSS以及正常融合。通过在Carla模拟环境和具有挑战性的UrbanNav场景中进行严格测试，证明了所提出算法的有效性。评估包括定性和定量分析，结果表明该方法具有鲁棒性和准确性。
