Performance Improvement of SLAM Based on Global Registration Using LiDAR Intensity and Measurement Data of Puddle

Abstract

The accuracy of scan matching-based SLAM strongly depends on the result of the initial alignments. In this paper, we improve the accuracy of scan matching-based SLAM by applying accurate initial alignments calculated by global registration using measurements from LiDAR intensity and water puddles as features, which are often found in damaged nuclear power plants. From the experimental results in the real environment, the proposed method can improve the accuracy of the map and the trajectory of the robot by taking these features observed from the environment into account.