NV-LIO: LiDAR-Inertial Odometry using Normal Vectors Towards Robust SLAM in Multifloor Environments

TL;DR

NV-LIO introduces a normal vector-based LiDAR-inertial odometry framework tailored for indoor multifloor environments, improving point cloud registration robustness and loop closure accuracy in challenging indoor SLAM scenarios.

Contribution

The paper presents a novel normal vector extraction and analysis method for LiDAR scans, enhancing registration and degeneracy handling in indoor multifloor SLAM.

Findings

Improved registration accuracy in indoor environments.

Effective degeneracy detection and adjustment for normal vector matching.

Successful validation on public and proprietary datasets.

Abstract

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.