NV-LIOM: LiDAR-Inertial Odometry and Mapping Using Normal Vectors Towards Robust SLAM in Multifloor Environments

Over the last few decades, numerous LiDAR-inertial odometry (LIO) algorithms have been developed, demonstrating satisfactory performance across diverse environments. Most of these algorithms have predominantly been validated in open outdoor environments; however, they often encounter challenges in confined indoor settings. In such indoor environments, reliable point cloud registration becomes problematic due to the rapid changes in LiDAR scans and repetitive structural features like walls and stairs, particularly in multifloor buildings. In this letter, we present NV-LIOM, a normal vector-based LiDAR-inertial odometry and mapping framework focused on robust point cloud registration designed for indoor multifloor environments. Our approach extracts the normal vectors from the LiDAR scans and utilizes them for correspondence search to enhance the point cloud registration performance. To ensure robust registration, the distribution of the normal vector directions is analyzed, and situations of degeneracy are examined to adjust the matching uncertainty. Additionally, a viewpoint-based loop closure module is implemented to avoid wrong correspondences that are blocked by the walls. The proposed method is tested through public datasets and our own dataset.