Outdoor Localization Using Stereo Vision Under Various Illumination Conditions

We present a mobile robot localization method using a stereo camera only. Vision-based localization in outdoor environments is still challenging issue because of large illumination changes. To cope with varying illumination conditions, we use 2D occupancy grid maps generated from 3D point clouds obtained by a stereo camera. Furthermore, we incorporate salient line segments extracted from the ground into the grid maps. The grid maps are not much affected by illumination conditions because occupancy information and salient line segments can be robustly obtained. On the grid maps, the robot poses are estimated using a particle filter that combines visual odometry and map-matching. We use edge point based stereo SLAM to obtain occupancy information and robot ego-motion estimation simultaneously. We tested our method under various illumination and weather conditions including sunny and rainy days. The experimental results showed the effectiveness and robustness of the proposed method. Our method enables localization under extremely poor illumination conditions which are too challenging for existing state-of-the-art methods.