In order to solve the problem of large positioning error and incomplete mapping of SLAM based on two-dimensional lidar in indoor environment,the paper presents a multi-sensor fusion SLAM algorithm for indoor robots.For the mismatch of the traditional ICP algorithm in the front end of the lidar SLAM,the PL-ICP algorithm is adopted that is more suitable for the indoor environment,and the extended Kalman filter is used to fuse the wheel odometer and IMU to provide the initial motion estimation value.During the mapping phase,the pseudo 2D laser data converted from the 3D point cloud data obtained by the depth camera is fused with the data obtained from the 2D lidar to remedy the defect that there is no vertical field of view in the 2D lidar mapping.The final experimental results show that the fusion odometer data has improved the positioning accuracy by at least 33％compared to a single wheeled odometer,providing a higher initial iteration value for the PL-ICP algorithm.And also,fusion mapping compensates for the weakness that a single two-dimensional lidar mapping has,and constructs an environmental map with more complete environmental information.
simultaneous localization and mappingindoor robotextended Kalman filterPL-ICP algorithm
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