Research on LiDAR road point cloud data completion based on edge information

Abstract

In the actual collection process of point cloud data, the measured road point cloud data is incomplete due to the influence of various factors such as collection equipment and object occlusion. This paper proposes a LiDAR road point cloud data completion algorithm based on edge information. The algorithm uses the OSM(OpenStreetMap) map information as structural and semantic prior knowledge to locate erroneous areas and extract road edge information. Then, the algorithm uses the road defective area edge information data sets as input to obtain the semantic information of road data. Finally, the algorithm realizes road point cloud map completion based on the nearest neighbor principle. Research in this area does not rely on the parameters of the hardware devices used for data acquisition, but uses the data itself as a driver to complete the data. Compared with traditional algorithms, this algorithm reduces the average nearest neighbor distance (Er) of road missing areas to 0.08m in road point cloud data completion, while this algorithm has better robustness and more accurate results for defective data completion.