  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 paper, we present NV-LIO, a normal vector based LIO
framework, designed for simultaneous localization and mapping (SLAM) in indoor
environments with multifloor structures. 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 propsed method is
validated through public datasets and our own dataset. To contribute to the
community, the code will be made public on https://github.com/dhchung/nv_lio.
