  In complex environments, low-cost and robust localization is a challenging
problem. For example, in a GPSdenied environment, LiDAR can provide accurate
position information, but the cost is high. In general, visual SLAM based
localization methods become unreliable when the sunlight changes greatly.
Therefore, inexpensive and reliable methods are required. In this paper, we
propose a stereo visual localization method based on the prior LiDAR map.
Different from the conventional visual localization system, we design a novel
visual optimization model by matching planar information between the LiDAR map
and visual image. Bundle adjustment is built by using coplanarity constraints.
To solve the optimization problem, we use a graph-based optimization algorithm
and a local window optimization method. Finally, we estimate a full six degrees
of freedom (DOF) pose without scale drift. To validate the efficiency, the
proposed method has been tested on the KITTI dataset. The results show that our
method is more robust and accurate than the state-of-art ORB-SLAM2.
