EllipseLIO让激光雷达惯性里程计自适应不同环境与传感器，无需人工调参。
EllipseLIO: Adaptive LiDAR Inertial Odometry with an Ellipsoid Representation
- 用椭球模型动态调整激光扫描滤波与配准，自动适配传感器和环境。
- 在5个数据集上平均误差比第二名低35%，所有实验均未发散。
- 适合需要跨场景部署的无人车、无人机等自主导航系统。
激光雷达惯性里程计（LIO）是移动机器人在无外部定位（如GPS）环境下自主导航的关键组件。面对不同环境与异构激光雷达传感器的平台，亟需一种无需人工干预即可自适应的LIO方法。现有方法在相似环境与传感器下可提供可靠精度，但在异构场景中难以保持鲁棒性。本文提出EllipseLIO，一种实时LIO方法，通过自适应的激光扫描滤波与配准策略，根据传感器性能和环境特性动态调整，实现无需场景特定调参的泛化能力。在包含五个多样化挑战性场景的数据集上的实验表明，EllipseLIO整体表现最优：平均误差比次优方法降低35%，且所有实验中均未出现发散。代码已开源：https://github.com/v4rl-ucy/ellipselio。
原文摘要 · Abstract (English)
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.
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