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：在过去的几十年里，已经开发出众多激光雷达-惯性里程计算法，并在各种环境中展现出令人满意的性能。然而，这些算法主要是在开阔的室外环境中进行验证，在狭小的室内场景中常常遇到挑战。在此类室内环境中，由于激光雷达扫描数据的快速变化以及墙壁、楼梯等重复性结构特征的存在，可靠的点云配准变得困难，这在多楼层建筑中尤为突出。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，专为具有多楼层结构的室内环境中的同步定位与建图任务而设计。我们的方法从激光雷达扫描中提取法向量，并利用它们进行对应点搜索以提升点云配准性能。为确保鲁棒的配准，我们分析了法向量方向的分布，并检查退化情况以调整匹配不确定性。此外，实现了一个基于视点的闭环检测模块，以避免被墙壁遮挡的错误对应关系。所提方法通过公开数据集和我们自行采集的数据集进行了验证。为促进学术共享，代码将在 https://github.com/dhchung/nv_lio 公开。