Research On Key Technologies Of Environment Understanding Of Ground Intelligent Robot Based On Lidar

Ground moving intelligent robot is a kind of equipment which can move automatically both in indoor and outdoor environment. It integrates technique of environment apperceiving and understanding, dynamic decision-making, dynamic path planning, action control and implementation, etc. Research of ground intelligent robot is an active area of high technology for lots of countries. Environment understanding is very important for a robot to navigate itself automatically. Lidar is a kind of active range finder. It is a kind of primary sensors in robotics as illumination has no effect to it.This dissertation focuses on key technologies of environment understanding of intelligent robot based on lidar. The research area of this dissertation including low-level data fusion, point cloud clustering, lidar scan-matching, environment feature extraction and traversable area detection. Several types of lidar that usually equipped on robots are used in this dissertation’s study, including single row range finder, muti-line lidar,3D scan lidar and PMD lidar.The mainly studying results of this dissertation are as follows:This dissertation proposes a point cloud clustering algorithm which based both on density and spatial distribution. The algorithm combines robust information-theoretic clustering method with DBS CAN algorithm. It uses the value of local volume after compressing to judge the clustering result. The algorithm computes radius of a point’s neighbor adaptively. It can differentiate points which have similar density but different spatial distribution.A traversable area detection algorithm based on lidar data is proposed. It employs a fuzzy cluster algorithm combined with traversable features to find traversable area in a single scan line, and then the algorithm considers space-time association between scan frames or scan lines to refine the extraction results of traversable area.An arrow shape registration board is designed to register a lidar and a camera to get colored point cloud.The dissertation uses a multi-feature vector to classify dense colored point cloud collected by a 3D scan lidar with a camera. The multi-feature vector contains both geometrical features and color feature. The algorithm trains a terrain classifier by using this multi-feature vector and gets better terrain classifying results than method using only geometrical feature. The dissertation studies scan-matching algorithms. Point-line and point-plane based scan-matching algorithms are proposed to match scans of a single row ranger finder or a 3D lidar. The algorithm finds line or plane feature in lidar data and associates them according to their general-distance. The rotations and translations are estimated respectively by associating line or plane feature and find matched points to decrease iterative computing.