EllipseLIO: 一种基于椭球表示的自适应激光雷达惯性里程计
EllipseLIO: Adaptive LiDAR Inertial Odometry with an Ellipsoid Representation
AI总结：
本文提出EllipseLIO，一种基于椭球表示的实时激光雷达惯性里程计，通过自适应的激光雷达扫描过滤和配准方法，在不同环境和传感器下实现鲁棒的里程计性能，实验表明其在多种复杂场景中表现最优。
摘要：
激光雷达惯性里程计（LIO）是许多需要无外部定位（如GPS）导航的移动机器人中的关键组件。在不同环境中自主运行且配备异构激光雷达传感器的平台需要一种能够适应这些不同场景且无需人工干预的LIO方法。现有LIO方法通常在环境和传感器相似时能提供可靠且准确的里程计，但许多方法在异构环境和传感器中保持鲁棒性时面临困难。本文提出了EllipseLIO，一种实时LIO方法，通过使用适应于传感器能力和环境的激光雷达扫描过滤和配准方法，在不同场景间进行泛化。在五个具有多样性和挑战性的数据集上，EllipseLIO与最先进的LIO方法的实验表明，EllipseLIO总体表现最佳。它在平均上比第二好的方法的里程计误差低38%，并且是唯一一个在所有实验中均不发散的方法。EllipseLIO的开源版本将在github.com/v4rl-ucy/ellipselio上提供。
英文摘要：
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.
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。
