Abstract
Laser range finder (LRF) or laser distance sensor (LDS), further referred to as LIDAR (light detection and ranging). LIDAR can obtain environmental point cloud data, while a robot can realize environmental sensing by adoption of the point cloud data generated and LIDAR-based SLAM (Simultaneous Localization And Mapping) algorithm. The precision of point clouds provided by the LIDAR determines that of environmental sensing of the LIDAR-based mobile robot. In this paper, a common correction algorithm has been proposed to correct the inaccuracy of measured point cloud data caused by mobile LIDAR, effectively improving the precision of point cloud data measured by the LIDAR under a mobile state. It also conducts mathematical derivation of the algorithm, presents simulation and real world experiments performed and verifies the necessity and effectiveness of the algorithm derived by experimental results in the paper.
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