GaRLILEO：基于重力对齐的雷达-腿-惯性增强里程计
GaRLILEO: Gravity-aligned Radar-Leg-Inertial Enhanced Odometry
AI总结：
本文提出GaRLILEO，一种基于重力对齐的雷达-腿-惯性增强里程计框架，通过连续时间 ego-velocity spline 和软 S2-constrained 重力因子提升垂直姿态估计精度，适用于复杂地形导航。
摘要：
腿部机器人在挑战性地形（如台阶、斜坡和无结构环境）中的部署日益受到青睐，相较于轮式平台。在这种情况下，准确的里程计估计是稳定运动、定位和制图的先决条件。传统本体感觉方法依赖于腿部运动学传感器模态和惯性传感，但频繁接触冲击、脚滑和振动导致不可抑制的垂直漂移，尤其受不准确的滚转和俯仰估计影响。现有方法结合了外周感觉传感器如激光雷达或相机。进一步增强通过利用重力矢量估计来添加对滚转和俯仰的额外观测，从而提高垂直姿态估计的准确性。然而，这些方法在特征稀疏或重复场景中性能下降，并易受双积分IMU加速度误差影响。为了解决这些挑战，我们提出GaRLILEO，一种新的基于重力对齐的连续时间雷达-腿-惯性里程计框架。GaRLILEO通过构建连续时间ego-velocity spline从SoC雷达多普勒和腿部运动学信息分离速度与IMU，实现无缝传感器融合，缓解里程计失真。此外，GaRLILEO能够可靠地捕捉准确的重力矢量，利用新的软S2约束重力因子，提高垂直姿态准确性，无需依赖激光雷达或相机。在自收集的现实世界数据集上评估，GaRLILEO展示了最先进的准确性，特别是在台阶和斜坡上的垂直里程计估计。我们开源了数据集和算法，以促进进一步的腿部机器人里程计和SLAM研究。
英文摘要：
Deployment of legged robots for navigating challenging terrains (e.g., stairs, slopes, and unstructured environments) has gained increasing preference over wheel-based platforms. In such scenarios, accurate odometry estimation is a preliminary requirement for stable locomotion, localization, and mapping. Traditional proprioceptive approaches, which rely on leg kinematics sensor modalities and inertial sensing, suffer from irrepressible vertical drift caused by frequent contact impacts, foot slippage, and vibrations, particularly affected by inaccurate roll and pitch estimation. Existing methods incorporate exteroceptive sensors such as LiDAR or cameras. Further enhancement has been introduced by leveraging gravity vector estimation to add additional observations on roll and pitch, thereby increasing the accuracy of vertical pose estimation. However, these approaches tend to degrade in feature-sparse or repetitive scenes and are prone to errors from double-integrated IMU acceleration. To address these challenges, we propose GaRLILEO, a novel gravity-aligned continuous-time radar-leg-inertial odometry framework. GaRLILEO decouples velocity from the IMU by building a continuous-time ego-velocity spline from SoC radar Doppler and leg kinematics information, enabling seamless sensor fusion which mitigates odometry distortion. In addition, GaRLILEO can reliably capture accurate gravity vectors leveraging a novel soft S2-constrained gravity factor, improving vertical pose accuracy without relying on LiDAR or cameras. Evaluated on a self-collected real-world dataset with diverse indoor-outdoor trajectories, GaRLILEO demonstrates state-of-the-art accuracy, particularly in vertical odometry estimation on stairs and slopes. We open-source both our dataset and algorithm to foster further research in legged robot odometry and SLAM. https://garlileo.github.io/GaRLILEO
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。
