  Depth estimation is one of the key technologies in some fields such as
autonomous driving and robot navigation. However, the traditional method of
using a single sensor is inevitably limited by the performance of the sensor.
Therefore, a precision and robust method for fusing the LiDAR and stereo
cameras is proposed. This method fully combines the advantages of the LiDAR and
stereo camera, which can retain the advantages of the high precision of the
LiDAR and the high resolution of images respectively. Compared with the
traditional stereo matching method, the texture of the object and lighting
conditions have less influence on the algorithm. Firstly, the depth of the
LiDAR data is converted to the disparity of the stereo camera. Because the
density of the LiDAR data is relatively sparse on the y-axis, the converted
disparity map is up-sampled using the interpolation method. Secondly, in order
to make full use of the precise disparity map, the disparity map and stereo
matching are fused to propagate the accurate disparity. Finally, the disparity
map is converted to the depth map. Moreover, the converted disparity map can
also increase the speed of the algorithm. We evaluate the proposed pipeline on
the KITTI benchmark. The experiment demonstrates that our algorithm has higher
accuracy than several classic methods.

Computer Vision and Pattern Recognition;Robotics