Vision-and-Lidar Based Real-time Outdoor Localization for Unmanned Ground Vehicles without GPS

This paper describes a vision-and-lidar based outdoor localization method for Unmanned Ground Vehicles (UGV) without GPS. We present a real-time method for pose estimation by combining visual odometry and lidar odometry. Instead of using a GPS, a laser scanner and a RGB-D camera are mounted on our UGV. Visual odometry and lidar odometry are fused by Extended Kalman Filter (EKF). Bundle Adjustment (BA) is used to optimize the fused odometry and build a 3D map. The method has been evaluated by four test routes set in our university. Experimental results indicate that a combination of visual and laser information on pose estimation is better than using only one of them. In addition to test robustness of the method, experiments are performed both in daytime and at dusk.

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