Reflective Markers Assisted Indoor LiDAR Self-Positioning Algorithm Based on Joint Optimization With FIM and GDOP

Simultaneous localization and mapping (SLAM) is one of the key technologies in autonomous driving and has been wildly used in mobile robot exploration and unmanned aerial vehicle navigation. Light detection and ranging (LiDAR) SLAM depends on matching the data perceived by LiDAR, finding the best match for consistency to determine the LiDAR’s pose and completing the map construction. However, most existing matching methods are inefficient. Moreover, each processing is based on the previous estimation result, estimation errors accumulate over time, resulting in cumulative drift error. To address issues of low efficiency in extracting key feature points, low self-positioning accuracy, and reliability, this article proposes a LiDAR self-positioning method based on reflective markers (RMs). First, the relationship between the positioning accuracy and the RMs’ coordinates is calculated through the Fisher information matrix (FIM). Then, geometric dilution of precision (GDOP) is introduced to comprehensively analyze the impacts of the geometric positions and quantity of the RMs on the positioning lower error bound of the LiDAR. The layout rules of RMs are designed according to the vertical angle resolution of the LiDAR resolution. We leverage the high reflectivity of RMs to calculate the LiDAR’s global position. By solving the distance, horizontal angle, and vertical angle information obtained from multiple RMs, high-precision self-positioning is achieved during the LiDAR’s motion. Experimental results show that, compared with the LiDAR Odometry and Mapping (LOAM) and LeGO-LOAM algorithms, the LiDAR self-positioning accuracy can be improved by approximately 50% in indoor environments, especially in indoor scenarios involving moving up and down stairs.