Beyond the Vehicle: Cooperative Localization by Fusing Point Clouds for GPS-Challenged Urban Scenarios

TL;DR

This paper introduces a cooperative localization method that fuses multi-modal sensor data, including point clouds from LiDAR and cameras, to improve vehicle positioning accuracy in GPS-challenged urban environments.

Contribution

It presents a novel multi-sensor fusion approach combining V2V, V2I, and point cloud SLAM techniques for enhanced urban vehicle localization.

Findings

Significantly improves localization accuracy in GPS-denied urban areas

Robustness against sensor noise and urban obstacles

Effective integration of infrastructure and vehicle data

Abstract

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.