融合车与路端点云，提升城市无信号环境定位精度
Beyond the Vehicle: Cooperative Localization by Fusing Point Clouds for GPS-Challenged Urban Scenarios
- 通过车车/车路通信融合多源点云数据
- 在复杂城市环境中定位误差显著降低
- 适合自动驾驶在遮挡严重区域使用
在GPS信号易受干扰的城市环境中，车辆精确定位仍是关键挑战。本文提出一种协同式多传感器、多模态定位方法，通过融合车车（V2V）和车路（V2I）系统数据，与基于点云配准的同步定位与地图构建（SLAM）算法相结合。系统处理来自车载激光雷达、双目相机以及路口部署传感器的点云数据。借助基础设施共享数据，该方法在复杂、高噪声的都市场景中显著提升了定位精度与鲁棒性。
原文摘要 · Abstract (English)
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.
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