3D point cloud map based vehicle localization using stereo camera

Nowadays, the driverless automobiles have become a near reality and are going to become widely available. For autonomous navigation, the vehicles need to precise localize itself within a pre-defined map. In this paper, we propose a novel algorithm for the problem of three-dimensional (3D) point cloud map (PCL) based localization using a stereo camera. This 3D point cloud map consists of dense 3D geometric information and intensity measures of surface reflectivity value generated by the 3D light detection and ranging (LIDAR) scanner based mapping system. Although some LIDAR based localization algorithms have been proposed, in this paper we present a comparable centimeter-level accuracy localization algorithm using much cheaper and commodity stereo camera. Specifically, at each candidate position we transform the 3D data points from the real-world coordinate system to the camera coordinate system and synthetic the virtual depth and intensity images from the 3D PCL map. We localize the ego vehicle by estimating the transformation between the real-world and vehicle coordinates in each frame by matching these virtual images with the stereo depth and intensity images. In the experiment part, we show that although the 3D map was generated 3 years ago, the proposed algorithm still can produce reliable localization results even in many difficult cases, such as shadow, dynamic objects, new lane marker and night.