无需标定，靠自学习实现腿式机器人鲁棒位姿估计
Online Learning of Robust Legged Odometry with Minimal Exteroceptive Supervision
- 用外感知信号做持续监督，在线训练本体感知速度神经网络
- 环境失效时自动切换到学习到的本体模型，保持定位稳定
- 适配多种四足平台，部署简单且无需硬件定制
腿式机器人稳健运动与导航严重依赖可靠的位姿估计。传统多传感器融合需精细的传感器标定和平台特异性运动学建模，部署复杂。工业级外感知传感器虽能提供高精度运动追踪，但在感知退化环境下仍易失效。为此，我们提出一种即插即用、鲁棒的腿式位姿估计系统，无需显式的外-本体标定或系统运动学建模。该方法利用成熟的外感知运动处理流水线作为连续监督信号，直接从本体感知数据训练在线学习的速度神经网络。随后采用不变性扩展卡尔曼滤波器（InEKF）融合学习得到的本体或外感知速度（如有）与惯性测量单元（IMU）数据。当环境导致外感知失效时，系统无缝切换至学习到的本体模型，实现对新硬件快速适应的韧性位姿估计。我们在不同四足平台上验证了该方法的平台无关性与可部署性，在复杂场景下展现出优异的运动估计鲁棒性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。