An Indoor Mapping Algorithm Fusing LiDAR-IMU Tightly Coupled Fusion and Scan Context: IS-LEGO-LOAM

Indoor environments often contain numerous areas with sparse structural features, such as long corridors, large atriums, and glass curtain walls, and other scenarios. These conditions can lead to difficulties in loop closure detection and accumulated positioning errors, resulting in localization drift or even mapping failure during map construction. This paper proposes an indoor mapping algorithm called IS-LEGO-LOAM that integrates tightly coupled LiDAR-IMU fusion and Scan Context. A tightly coupled LiDAR-IMU odometry is constructed, and an adaptive covariance matrix is designed to solve the problems of abnormal LiDAR echoes and insufficient effective feature extraction caused by sparse indoor feature points. By introducing the Scan Context global descriptor and adopting the strategies of vector nearest neighbor search and similarity score matching, the drift problem in large-scale scenes is alleviated. Finally, validation is performed on the KITTI dataset and in real-world scenarios, respectively. Experiments show that the improved IS-LEGO-LOAM achieves superior mapping performance.