An Indoor Laser Inertial SLAM Method Fusing Semantics and Planes

ABSTRACT

LiDAR‐based simultaneous localization and mapping (LiDAR SLAM) technology is widely used for high‐precision 3D mapping in complex environments, especially in the fields of non‐contact remote sensing and geographic information systems. However, affected by factors such as sensor errors and dynamic environment, SLAM methods are prone to accumulate errors, which affect its accuracy and reliability. In this article, we propose a LiDAR SLAM odometry optimization method, Semantic and Planar Constraint SLAM (SPC‐SLAM). The method strengthens the effectiveness of planar constraint by introducing semantic information and combines with factor graph optimization to improve the accuracy of key‐frame pose estimation. In addition, we design a pseudo‐truth‐based threshold judgment mechanism for deciding whether it is necessary to perform semantic segmentation steps to ensure the efficiency of SLAM as much as possible. We conducted comparative experiments on part of public data in SubT‐MRS Dataset and self‐acquired campus data. The results show that in the complex indoor environments we chose, LIO‐SAM is unable to complete the whole mapping under the initial parameters, and the overall absolute trajectory error of SPC‐SLAM is reduced by about 65% compared with the FAST‐LIO2, demonstrating the potential of the method for application in accurate indoor mapping and 3D imaging.