Accurate vehicle localization is a critical challenge in urban environments where GPS signals are often unreliable. This paper presents a cooperative multi-sensor and multi-modal localization approach to address this issue by fusing data from vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) systems. Our approach integrates cooperative data with a point cloud registration-based simultaneous localization and mapping (SLAM) algorithm. The system processes point clouds generated from diverse sensor modalities, including vehicle-mounted LiDAR and stereo cameras, as well as sensors deployed at intersections. By leveraging shared data from infrastructure, our method significantly improves localization accuracy and robustness in complex, GPS-noisy urban scenarios.

翻译：在GPS信号常不可靠的城市场景中，精确的车辆定位是一项关键挑战。本文提出一种协同多传感器多模态定位方法，通过融合车对车（V2V）与车对基础设施（V2I）系统的数据来解决此问题。该方法将协同数据与基于点云配准的同步定位与建图（SLAM）算法相结合。系统处理来自多种传感器模态生成的点云，包括车载激光雷达与立体相机，以及部署在交叉路口的传感器。通过利用基础设施共享的数据，本方法在复杂、GPS噪声严重的城市场景中显著提升了定位精度与鲁棒性。