Research on Feature Extraction Method Based on Point Cloud Roughness

Abstract

Simultaneous Localization and Mapping (SLAM) is a fundamental element of intelligent mobile robotics. An efficient and reliable point cloud feature extraction plays an important role in SLAM technology. This paper studies the feature extraction method of the point cloud in the process of constructing a high-precision map by the 3D lidar sensor, and proposes a method based on the point cloud. A valuable contribution of the proposed method is that the extracted point cloud features can be observed repeatedly. Through comparative experiments, it is verified that the number of extracted features based on point cloud segmentation is significantly reduced, the calculation time is significantly reduced, and it is more conducive to the subsequent point cloud feature registration. Therefore, the method proposed in this paper provides important enlightenment for improving the accuracy and efficiency of constructing a high-precision map of an intelligent mobile robot by 3D lidar.