For field robotic missions such as inspection, search-and-rescue, and exploration, light detection and ranging (LiDAR)-inertial odometry (LIO) can serve as a core component of autonomy by providing localization and mapping in GNSS-denied or unstructured environments. However, transitions between confined and open spaces, which are commonly encountered in field deployments, can induce substantial changes in scan density and local geometric structure, thereby reducing the robustness and computational efficiency of LIO. To address these issues, we present GenZ-LIO, a generalizable LIO framework designed to adapt to variations in spatial scale across confined and open environments. GenZ-LIO comprises three components: (i) scale-aware adaptive voxelization for regulating scan downsampling across spatial scale changes, (ii) hybrid-metric state update for combining point-to-plane and point-to-point residuals under varying geometric structure, and (iii) voxel-pruned correspondence search for efficient point-to-point matching. We conduct a comprehensive evaluation using 42 sequences from nine public datasets and our newly collected NarrowWide dataset to analyze LIO performance under spatial scale variations across diverse field scenarios. Across the evaluated sequences, GenZ-LIO maintains stable odometry estimation without divergence, indicating practical robustness under the tested field conditions. The source code and collected dataset will be made publicly available upon publication.

翻译：面向巡检、搜索救援及勘探等野外机器人任务，激光雷达-惯性里程计可作为GNSS拒止或非结构化环境中提供定位与建图的核心自主导航组件。然而，在野外部署中常见的受限空间与开阔空间之间的过渡，会引发扫描点云密度与局部几何结构的显著变化，从而降低LIO的鲁棒性与计算效率。为解决上述问题，我们提出GenZ-LIO——一种通用化LIO框架，旨在自适应调节受限与开放环境下空间尺度变化带来的影响。GenZ-LIO包含三大组件：（i）尺度感知自适应体素化，用于调控空间尺度变化下的扫描降采样；（ii）混合度量状态更新，用于融合不同几何结构下的点-面与点-点残差；（iii）体素剪枝对应搜索，用于实现高效的点-点匹配。我们使用来自九个公开数据集的42个序列及新采集的NarrowWide数据集进行综合评估，分析多种野外场景下空间尺度变化对LIO性能的影响。在所有评估序列中，GenZ-LIO均未发生发散并保持稳定的里程计估计，显示了其在测试野外条件下的实际鲁棒性。源代码与采集的数据集将在论文发表后公开发布。