Beyond the Vehicle: Cooperative Localization by Fusing Point Clouds for GPS-Challenged Urban Scenarios
This addresses localization issues for autonomous vehicles in urban areas with unreliable GPS, representing an incremental advancement in sensor fusion methods.
The paper tackles the problem of accurate vehicle localization in GPS-challenged urban environments by proposing a cooperative approach that fuses point clouds from vehicle-to-vehicle and vehicle-to-infrastructure systems, resulting in significant improvements in localization accuracy and robustness.
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.