ROI-cloud: A Key Region Extraction Method for LiDAR Odometry and Localization

Abstract

We present a novel key region extraction method of point cloud, ROI-cloud, for LiDAR odometry and localization with autonomous robots. Traditional methods process massive point cloud data in every region within the field of view. In dense urban environments, however, processing redundant and dynamic regions of point cloud is time-consuming and harmful to the results of matching algorithms. In this paper, a voxelized cube set, ROI-cloud, is proposed to solve this problem by exclusively reserving the regions of interest for better point set registration and pose estimation. 3D space is firstly voxelized into weighted cubes. The key idea is to update their weights continually and extract cubes with high importance as key regions. By extracting geometrical features of a LiDAR scan, the importance of each cube is evaluated as a new measurement. With the help of on-board IMU/odometry data as well as new measurements, the weights of cubes are updated recursively through Bayes filtering. Thus, dynamic and redundant point cloud inside cubes with low importance are discarded by means of Monte Carlo sampling. Our method is validated on various datasets, and results indicate that the ROI-cloud improves the existing method in both accuracy and speed.