arXiv 2603.162732026-10-07cs.RO

GenZ-LIO: 超越受限与开放边界的可泛化激光雷达-惯性里程计

GenZ-LIO: Generalizable LiDAR-Inertial Odometry Beyond Confined--Open Boundaries

Computational Control Engineering Laboratory (CoCEL), Department of Convergence IT Engineering and Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, South Korea（convergence 信息技术工程与电气工程系计算控制工程实验室，POSTECH 首尔大学（Pohang University of Science and Technology）, Pohang 37673, South Korea）；Laboratory for Information & Decision Systems (LIDS), Massachusetts Institute of Technology, Cambridge, MA 02139, USA（信息与决策系统实验室，麻省理工学院（Massachusetts Institute of Technology）, Cambridge, MA 02139, USA）

Daehan Lee, Hyungtae Lim, Seongjun Kim, Soonbin Rho, Changhyeon Lee, Sanghyun Park, Junwoo Hong, Eunseon Choi, Hyunyoung Jo, Soohee Han

备注：: 29 pages, 19 figures, Accepted to IEEE Transactions on Field Robotics (T-FR)

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AI总结：

提出GenZ-LIO框架，通过尺度自适应体素化、混合度量状态更新和体素剪枝对应搜索，解决受限与开放空间过渡导致的鲁棒性和效率下降问题，在42个序列上保持稳定估计。

摘要：

对于巡检、搜索救援和探索等现场机器人任务，激光雷达-惯性里程计（LIO）通过在GNSS拒绝或无结构环境中提供定位和建图，可作为自主性的核心组件。然而，现场部署中常见的受限与开放空间之间的过渡会导致扫描密度和局部几何结构发生显著变化，从而降低LIO的鲁棒性和计算效率。为解决这些问题，我们提出了GenZ-LIO，一个可泛化的LIO框架，旨在适应受限和开放环境中空间尺度的变化。GenZ-LIO包含三个组件：(i) 尺度感知自适应体素化，用于调节跨空间尺度变化的扫描下采样；(ii) 混合度量状态更新，用于在变化的几何结构下组合点到平面和点到点残差；(iii) 体素剪枝对应搜索，用于高效的点到点匹配。我们使用来自九个公共数据集的42个序列以及我们新收集的NarrowWide数据集进行了全面评估，以分析LIO在不同现场场景下空间尺度变化时的性能。在评估的序列中，GenZ-LIO保持稳定的里程计估计而不发散，表明在测试的现场条件下具有实际鲁棒性。源代码和收集的数据集将在发表后公开。

英文摘要：

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. Our code is available at https://github.com/cocel-postech/genz-lio .

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。