用雷达和足部运动数据，提升腿式机器人在楼梯斜坡上的定位精度。
GaRLILEO: Gravity-aligned Radar-Leg-Inertial Enhanced Odometry
- 通过连续时间速度样条融合雷达多普勒与足部运动信息，减少惯性传感器误差。
- 提出软S2约束重力因子，无需依赖激光或摄像头，在复杂场景中保持垂直定位准确。
- 在真实世界多地形数据集上表现优异，尤其在上下楼梯时优于现有方法。
腿式机器人在楼梯、斜坡及非结构化环境中的应用日益增多，而精准的里程计估计是实现稳定行走、定位与建图的前提。传统基于本体感知的方法依赖腿部运动学与惯性测量，易受频繁接触冲击、脚底打滑和振动影响，导致难以避免的垂直漂移，尤其在滚动角和俯仰角估计不准确时更显著。现有方法引入外部感知传感器如激光雷达或相机，部分通过重力向量估计增强对滚动和俯仰的观测，从而提升垂直位姿精度。然而，这些方法在特征稀疏或重复场景中性能下降，且易受双重积分惯性测量单元（IMU）加速度误差影响。为此，本文提出 GaRLILEO——一种新型重力对齐的连续时间雷达-足部-惯性里程计框架。该框架通过构建基于片上系统（SoC）雷达多普勒与足部运动信息的连续时间自身速度样条，解耦速度与IMU，实现无缝传感器融合，有效缓解里程计失真。同时，提出新颖的软S2约束重力因子，可靠估计重力向量，提升垂直位姿精度，无需依赖激光雷达或相机。在自收集的多样化室内外轨迹数据集上评估表明，GaRLILEO在楼梯和斜坡等场景下实现了最先进的垂直里程计精度。论文开源了数据集与算法代码，以促进腿式机器人里程计与SLAM研究。https://garlileo.github.io/GaRLILEO
原文摘要 · Abstract (English)
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
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