Registration of GPS and stereo vision for point cloud localization in intelligent vehicles using particle swarm optimization

Abstract

In this paper, we propose an algorithm for the registration of the GPS sensor and the stereo camera for vehicle localization within 3D dense point clouds. We adopt the particle swarm optimization algorithm to perform the sensor registration and the vehicle localization. The registration of the GPS sensor and the stereo camera is performed to increase the robustness of the vehicle localization algorithm. In the standard GPS-based vehicle localization, the algorithm is affected by noisy GPS signals in certain environmental conditions. We can address this problem through the sensor fusion or registration of the GPS and stereo camera. The sensors are registered by estimating the coordinate transformation matrix. Given the registration of the two sensors, we perform the point cloud-based vehicle localization. The vision-based localization is formulated as an optimization problem, where the “optimal” transformation matrix and corresponding virtual point cloud depth image is estimated. The transformation matrix, which is optimized, corresponds to the coordinate transformation between the stereo and point cloud coordinate systems. We validate the proposed algorithm with acquired datasets, and show that the algorithm robustly localizes the vehicle.