超越车辆：通过融合点云进行协作定位以应对GPS挑战的都市场景
Beyond the Vehicle: Cooperative Localization by Fusing Point Clouds for GPS-Challenged Urban Scenarios
AI总结：
本文提出一种融合点云的协作定位方法，通过多传感器和多模态数据提升GPS不可靠城市环境中的定位精度和鲁棒性。
摘要：
在城市环境中，准确的车辆定位是一个关键挑战，因为GPS信号通常不可靠。本文提出了一种协作多传感器和多模态定位方法，通过融合车辆对车辆（V2V）和车辆对基础设施（V2I）系统的数据来解决这一问题。我们的方法整合了协作数据与基于点云配准的同时定位与建图（SLAM）算法。系统处理由多种传感器模态生成的点云，包括车载激光雷达和立体相机，以及部署在交叉口的传感器。通过利用基础设施共享的数据，我们的方法显著提高了在复杂、GPS噪声的都市场景中的定位精度和鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
