Light detection and ranging (LiDAR)-inertial odometry (LIO) enables accurate localization and mapping for autonomous navigation in various scenes. However, its performance remains sensitive to variations in spatial scale, which refers to the spatial extent of the scene reflected in the distribution of point ranges in a LiDAR scan. Transitions between confined indoor and expansive outdoor spaces induce substantial variations in point density, which may reduce robustness and computational efficiency. To address this issue, we propose GenZ-LIO, a LIO framework generalizable across both indoor and outdoor environments. GenZ-LIO comprises three key components. First, inspired by the principle of the proportional-integral-derivative (PID) controller, it adaptively regulates the voxel size for downsampling via feedback control, driving the voxelized point count toward a scale-informed setpoint while enabling stable and efficient processing across varying scene scales. Second, we formulate a hybrid-metric state update that jointly leverages point-to-plane and point-to-point residuals to mitigate LiDAR degeneracy arising from directionally insufficient geometric constraints. Third, to alleviate the computational burden introduced by point-to-point matching, we introduce a voxel-pruned correspondence search strategy that discards non-promising voxel candidates and reduces unnecessary computations. Experimental results demonstrate that GenZ-LIO achieves robust odometry estimation and improved computational efficiency across confined indoor, open outdoor, and transitional environments. Our code will be made publicly available upon publication.

翻译：激光雷达-惯性里程计（LIO）能够在各种场景中为自主导航提供精确的定位与建图。然而，其性能仍对空间尺度的变化敏感，空间尺度指的是激光雷达扫描中点云距离分布所反映的场景空间范围。从狭窄的室内空间到开阔的室外空间的转换会导致点云密度发生显著变化，这可能降低系统的鲁棒性和计算效率。为解决此问题，我们提出了GenZ-LIO，一个可在室内和室外环境中通用的LIO框架。GenZ-LIO包含三个关键组件。首先，受比例-积分-微分（PID）控制器原理启发，它通过反馈控制自适应地调节用于下采样的体素大小，驱使体素化后的点云数量趋向于一个基于尺度信息的设定点，从而实现在不同场景尺度下稳定高效的处理。其次，我们构建了一种混合度量状态更新方法，联合利用点到平面和点到点残差，以缓解因方向性几何约束不足而产生的激光雷达退化问题。第三，为减轻点到点匹配带来的计算负担，我们引入了一种体素剪枝的对应关系搜索策略，该策略摒弃无前景的体素候选者，从而减少不必要的计算。实验结果表明，GenZ-LIO在狭窄室内、开阔室外及过渡环境中均实现了鲁棒的里程计估计和更高的计算效率。我们的代码将在论文发表后公开。