Ground-Texture-Based Localization for Intelligent Vehicles

Localization is a critical problem in the research of intelligent vehicles. Although it can be achieved by using a real-time kinematic global positioning system (RTK-GPS, or fused with other methods such as dead reckoning), it may be unfeasible if every vehicle has to be equipped with such an expensive sensor. This paper proposes a ground-texture-based map-matching approach to address the localization problem. To reduce the effect of complicated illumination in outdoor environments, a camera is fixed downward at the bottom of a vehicle, and controllable lights are also equipped around the camera for consistent illumination. The proposed approach includes two steps: 1) mapping and 2) localization. RTK-GPS is only used in the mapping, and other sensor data from camera and odometry are captured with time stamps to create a global ground texture map. A multiple-view registration-based optimization algorithm is applied to improve map accuracy. In the localization step, vehicle pose is estimated by matching the current camera frame with the best submap frame and by fusion strategy. Results with both synthetic and real experiments prove the feasibility and effectiveness of the proposed approach.