外观
论文arXiv 2605.211502 个月前
EllipseLIO：采用椭球表示的自适应 LiDAR 惯性里程计
原文链接：EllipseLIO: Adaptive LiDAR Inertial Odometry With an Ellipsoid Representation（arXiv 2605.21150 · 发布日期：2026-08-21）
EllipseLIO: Adaptive LiDAR Inertial Odometry With an Ellipsoid Representation
针对异构环境和 LiDAR 传感器下 LIO 难以稳健泛化，EllipseLIO 通过自适应扫描滤波与配准，在五个数据集上验证。
核心要点
- 移动机器人无外部定位导航依赖 LIO，但现有方法跨环境和传感器鲁棒性不足。
- EllipseLIO 让扫描滤波与配准随传感器能力和环境自适应，无需场景特定调参。
- 在五个多样且具挑战性数据集上整体最佳，较次优平均误差低35%，且唯一未发散。
- EllipseLIO 已提供开源版本，摘要中给出了 GitHub 地址。
验证范围与局限：验证范围为五个多样且具挑战性数据集上的实验。
英文摘要 (Abstract)
LiDAR Inertial Odometry (LIO) is a critical component for many mobile robots that need to navigate without relying on external positioning (e.g., GPS). Platforms that operate autonomously in different environments and with heterogeneous LiDAR sensors require a LIO approach that can adapt to these different scenarios without human intervention. Existing LIO approaches can typically provide reliable and accurate odometry in scenarios with similar environments and sensors when suitably tuned. However, many approaches struggle to retain robust odometry across heterogeneous environments and sensors while using a consistent configuration. This paper presents EllipseLIO, a real-time LIO approach that generalises between scenarios by using methods for LiDAR scan filtering and registration that adapt to the sensor capabilities and environment without requiring scenario-specific tuning. Experiments with EllipseLIO and state-of-the-art LIO approaches on five datasets with diverse and challenging scenarios demonstrate that EllipseLIO is the best performing approach overall. It achieves a 35% lower odometry error on average than the second-best approach and is the only approach that does not diverge in any experiment. An open-source version of EllipseLIO is available at https://github.com/v4rl-ucy/ellipselio.
作者：Rowan Border, Margarita Chli
来源
本页事实对应的公开页面，以原文为准。