最小外感受监督下的鲁棒腿式里程计在线学习
Online Learning of Robust Legged Odometry with Minimal Exteroceptive Supervision
AI总结：
提出一种即插即用的鲁棒腿式里程计系统，利用外感受运动管线作为监督信号在线学习速度神经网络，并结合不变扩展卡尔曼滤波器融合数据，无需传感器标定或运动学建模，在不同四足平台上验证了其适应性和鲁棒性。
摘要：
腿式机器人的鲁棒运动与导航高度依赖于可靠的里程计。传统的多传感器融合状态估计需要精细的传感器标定和平台特定的运动学建模，这增加了部署的复杂性。工业封装的外感受传感器可以提供精确的运动跟踪，但在感知退化条件下仍然脆弱。因此，我们开发了一种即插即用的鲁棒腿式里程计系统，消除了对外感受-本体感受标定或系统运动学建模的需求。我们的方法利用已有的外感受运动管线作为连续监督信号，直接从本体感受数据在线学习速度神经网络。然后使用不变扩展卡尔曼滤波器（InEKF）融合学习到的本体感受或外感受速度（如果有）以及IMU数据。当外感受因环境退化而失效时，系统无缝切换到使用学习到的本体感受模型，从而产生能够快速适应新硬件的弹性腿式里程计。我们在不同的四足平台上展示了我们方法的平台无关性和易于部署的特性，在挑战性场景中展示了保持鲁棒运动估计的有希望的结果。
英文摘要：
Robust locomotion and navigation for legged robots relies heavily on dependable odometry. Traditional multi-sensor fusion for such state estimation requires meticulous sensor calibration and platform-specific kinematic modeling, which complicates deployment. Industrially packaged exteroceptive sensors can provide accurate motion tracking but remain vulnerable to perceptually degraded conditions. We thus develop a plug-and-play, robust legged odometry system that eliminates the need for explicit exteroceptive-to-proprioceptive calibration or system kinematic modeling. Our approach leverages established exteroceptive motion pipelines as a continuous supervisory signal to train an online learned velocity neural network directly from proprioceptive data. An Invariant EKF (InEKF) is then used to fuse the learned proprioceptive or exteroceptive velocity (if any) and IMU data. When exteroception fails due to environmental degradation, the system seamlessly falls back to using the learned proprioceptive model, yielding a resilient legged odometry that readily adapts to new hardware. We demonstrate the platform-agnostic, easily deployable nature of our approach on different quadruped platforms, showcasing promising results in maintaining robust motion estimation across challenging scenarios.
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。
