Intensity Assisted LiDAR Self-Positioning for Indoor Environment

Simultaneous Localization and Mapping (SLAM) technology based on light detection and ranging (LiDAR) is one of the mainstream solutions to achieve self-positioning of unmanned platforms. However, accumulated drift errors can cause large interference to self-positioning of the SLAM algorithms. To address this problem, we propose a LiDAR-based positioning framework for 3D objects based on a reflective marker in indoor scenes. An intensity based filtering algorithm is proposed to extract the reference points of the reflective marker, and the azimuth and distance information of the reference points is used to calculate the global position of the LiDAR, which can correct the offset errors brought by the odometry of SLAM algorithms. Experiment results demonstrate the effectiveness of the proposed method.