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Aiming at the obstacle recognition problem of the detector in the soft landing process of the extraterrestrial surface, a method of fusing 3D point cloud and grayscale image for accurate obstacle recognition is proposed. Firstly, the grayscale image and the 3D point cloud are normalized to the same coordinate system by coordinate transformation. Then the K-means clustering algorithm is used to the pre-processed grayscale image to generate the optical obstacle map. Finally, the 3D point cloud which generated by the lidar is processed by the open source library PCL (Point Cloud Library).The RASAC (Random Sample Consensus, RASAC) algorithm is adopted to extracts the plane of the landing zone. After removing the plane from the point cloud, a segmentation algorithm is used to separate obstacles such as protrusions and pits. By the convert matrix from lidar coordinate to camera coordinate, the obstacles are projected to the grayscale image which the final obstacle map is generated.