用连续可信度取代二元接触判断，提升足式机器人自洽定位精度
FOCUS: Foot Observation Confidence for Robust Humanoid Proprioceptive Odometry
- 提出连续足部观测可信度预测，替代传统二元接触判断
- 仿真训练下实现83.7%轨迹误差降低，真实场景减错70.8%
- 仅需IMU与关节数据，适配无扭矩传感器的硬件平台
足部正运动学（FK）广泛用于提升足式机器人本体感知里程计性能，通过提供可靠的速率约束。现有接触辅助估计算法通常依赖二元接触判断决定是否信任整足的FK测量值，但接触并不等同于FK可靠性。动态行走常伴随部分支撑、脚尖拖拽和滑动，导致二元接触判断在长轨迹中累积显著漂移。为此，本文提出FOCUS（基于未标注仿真的足部观测可信度），为每只脚预测连续的FK可靠性权重，而非估计二元接触状态。该模型不替换原有基于模型的估计算法，而是将预测的可靠性权重用于融合FK速度观测与惯性测量单元（IMU）传播的体速度，并调节扩展卡尔曼滤波器（EKF）的观测协方差，实现平滑的可靠性感知融合，避免硬切换。网络通过自动生成的仿真信号训练，采用带轻量级仿真接触正则化的FK加权速度一致性损失，无需人工标注的连续可靠性标签。部署模型仅依赖IMU和关节运动学测量，适用于扭矩传感不可靠的硬件平台。实验表明，FOCUS在模拟步行片段中使绝对轨迹误差（ATE）降低83.7%，保持动态运动尺度与频谱能量的仿真保真度；在19段真实步行片段中减少ATE 70.8%；在四组真实动态运动中平均减少ATE 42.7%。
原文摘要 · Abstract (English)
Foot forward kinematics (FK) is widely used to improve proprioceptive legged odometry by providing reliable velocity constraints during foot support. Existing contact-aided estimators generally rely on binary contact decisions to determine whether the FK measurements of an entire foot should be trusted. However, contact does not necessarily imply FK reliability. Dynamic locomotion often involves partial support, toe dragging, and foot slip, causing binary contact decisions to accumulate significant drift over long trajectories. To address this limitation, we propose FOCUS (Foot Observation Confidence from Unannotated Simulation), which predicts a continuous FK reliability weight for each foot instead of estimating binary foot contact. Rather than replacing the model-based estimator, the predicted reliability weights are used to blend FK velocity observations with IMU-propagated body velocity and to adapt the observation covariance of an extended Kalman filter (EKF), enabling smooth reliability-aware fusion without hard contact switching. The network is trained from automatically generated simulation signals using an FK-weighted velocity consistency loss with lightweight simulator-contact regularization, without manually annotated continuous FK-reliability labels. The deployed model relies only on IMU and joint kinematic measurements, making it suitable for hardware platforms with unreliable torque sensing. Experiments demonstrate that FOCUS reduces absolute trajectory error (ATE) by 83.7% on simulated walking episodes, preserves simulated dynamic-motion fidelity in motion scale and spectral energy, reduces ATE by 70.8% across 19 real walking segments, and reduces mean ATE by 42.7% across four real dynamic-motion routines.
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