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.

翻译：近数十年来，多种激光雷达-惯性里程计算法被提出，并在不同环境中展现出令人满意的性能。然而，大多数算法主要在开阔的室外环境中得到验证，在受限的室内场景中常面临挑战。在此类室内环境中，由于激光雷达扫描的快速变化以及墙壁、楼梯等重复性结构特征——尤其在多楼层建筑中——可靠的点云配准变得困难。本文提出NV-LIO，一种基于法向量的激光雷达-惯性里程计框架，专为多楼层结构室内环境下的同时定位与地图构建而设计。我们的方法从激光雷达扫描中提取法向量，并利用其进行对应点搜索以提升点云配准性能。为确保鲁棒配准，分析了法向量方向的分布，并检查退化情形以调整匹配不确定性。此外，实现了基于视点的闭环模块，以避免被墙壁遮挡的错误对应。所提方法通过公开数据集及自有数据集得到验证。为促进社区发展，代码将在https://github.com/dhchung/nv_lio公开。