AdaLIO：退化室内环境中的鲁棒自适应激光雷达-惯性里程计
AdaLIO: Robust Adaptive LiDAR-Inertial Odometry in Degenerate Indoor Environments
AI总结：
针对固定参数使 LiDAR-惯性里程计在走廊等狭窄室内场景退化发散的问题，本文提出 AdaLIO，通过识别类走廊环境并自适应调整体素化与法向量估计参数来增加对应关系，在公开数据集上验证了鲁棒性。
摘要：
近年来，为了对环境进行建模以实现高效的场地管理，使用光探测与测距（LiDAR，激光雷达）传感器绘制建筑工地或建筑物地图的需求不断增加。然而，人们观察到，在螺旋楼梯和走廊等狭窄、受限的环境中，基于 LiDAR 的方法有时会发散，其原因是参数固定，未能随环境变化而调整。也就是说，LiDAR（-惯性）里程计的参数大多是为开放空间设置的；因此，如果将适用于开放空间的相同参数应用于类似走廊的场景，就会导致里程计方法发散，这被称为“退化”。为解决这一退化问题，我们提出了一种名为 AdaLIO 的鲁棒 LiDAR-惯性里程计，该方法采用自适应参数设置策略。为此，我们首先通过判断周围环境是否为类似走廊的环境来检查退化情况。如果是，则自适应地改变与体素化和法向量估计相关的参数，以增加对应关系的数量。在一个公开数据集上的验证表明，我们提出的方法在狭窄、拥挤的环境中表现出良好性能，避免了退化问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
