LAG-IO: 脚式ロボットのためのマルチIMU因子グラフ状態推定
LAG-IO: Multi-IMU State Estimation for Legged Robots Using Factor Graphs
脚に取り付けた複数のIMUと関節エンコーダを因子グラフで統合し、KDEベースの接触検出でZUPTを適応制御する脚式ロボットの状態推定手法を提案。
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著者: Yicheng Zhou, Longwen Chen, Qinghua Yu, Junhao Xiao, Huimin Lu, Xieyuanli Chen
分類: cs.RO
原文アブストラクト
Robust proprioceptive state estimation is crucial for legged robots, particularly in challenging environments where exteroceptive sensors are unreliable. While traditional leg odometry typically relies on a single body-mounted inertial measurement unit (IMU) fused with leg kinematics via an extended Kalman filter (EKF), such methods are vulnerable to linearization errors and hard-contact assumptions, leading to severe drift during slippage or dynamic locomotion.In this paper, we propose LAG-IO, a factor graph-based multi-IMU state estimation framework that tightly couples measurements from a main body IMU, multiple leg-mounted IMUs, and joint encoders within a sliding-window optimization. To handle non-stationary foot-ground interactions, we introduce a continuous probabilistic contact detection module based on kernel density estimation (KDE), which adaptively scales the covariance of zero-velocity update (ZUPT) factors. Furthermore, kinematic relative position factors are formulated to structurally anchor the independent leg inertial states to the robot's main body. Experimental results on a quadruped robot across various unstructured terrains demonstrate that, compared to existing baselines, LAG-IO achieves higher planar trajectory accuracy, reduces unobservable vertical divergence, and provides smoother trajectory transitions during unstable contacts, while maintaining competitive attitude observability.