In recent years, the demand for mapping construction sites or buildings using light detection and ranging~(LiDAR) sensors has been increased to model environments for efficient site management. However, it is observed that sometimes LiDAR-based approaches diverge in narrow and confined environments, such as spiral stairs and corridors, caused by fixed parameters regardless of the changes in the environments. That is, the parameters of LiDAR (-inertial) odometry are mostly set for open space; thus, if the same parameters suitable for the open space are applied in a corridor-like scene, it results in divergence of odometry methods, which is referred to as \textit{degeneracy}. To tackle this degeneracy problem, we propose a robust LiDAR inertial odometry called \textit{AdaLIO}, which employs an adaptive parameter setting strategy. To this end, we first check the degeneracy by checking whether the surroundings are corridor-like environments. If so, the parameters relevant to voxelization and normal vector estimation are adaptively changed to increase the number of correspondences. As verified in a public dataset, our proposed method showed promising performance in narrow and cramped environments, avoiding the degeneracy problem.

翻译：近年来，利用激光雷达传感器对建筑工地或建筑物进行建图的需求持续增长，以构建环境模型从而实现高效的场地管理。然而，研究发现，在狭窄封闭环境（如螺旋楼梯和走廊）中，由于使用固定参数而无法适应环境变化，基于激光雷达的方法有时会出现发散问题。即激光雷达（-惯性）里程计的参数主要针对开阔空间设置；因此，若将适用于开阔空间的同一参数应用于走廊类场景，会导致里程计方法发散，这种现象称为退化。为解决此问题，我们提出了一种名为 AdaLIO 的鲁棒激光雷达惯性里程计，该算法采用自适应参数调整策略。为此，我们首先通过判断周围环境是否为走廊类场景来检测退化状态。若检测到退化，则自适应调整体素化与法向量估计相关参数，以增加对应点数量。在公开数据集上的验证表明，所提方法在狭窄局促环境中展现出优越性能，有效避免了退化问题。