# LAG-IO：使用因子图的腿式机器人多 IMU 状态估计

> 原文链接：https://robotics.hellobike.com/papers/2026-10-13-2610-17870
> 频道：papers | 更新日期：2026-10-13 | 标签：arxiv, cs.RO
> 核心摘要：针对腿式机器人本体状态估计漂移，LAG-IO用因子图紧耦合多IMU与关节编码器，并在四足机器人非结构地形实验验证。

# LAG-IO：使用因子图的腿式机器人多 IMU 状态估计

::: v-pre
> 原文链接：[LAG-IO: Multi-IMU State Estimation for Legged Robots Using Factor Graphs](https://arxiv.org/abs/2610.17870)（arXiv 2610.17870 · 发布日期：2026-10-13）

**LAG-IO: Multi-IMU State Estimation for Legged Robots Using Factor Graphs**

针对腿式机器人本体状态估计漂移，LAG-IO用因子图紧耦合多IMU与关节编码器，并在四足机器人非结构地形实验验证。

## 核心要点

- 传统单机身IMU结合腿运动学的EKF在打滑或动态运动中易漂移。
- LAG-IO在滑动窗口因子图中紧耦合机身IMU、腿部IMU和关节编码器。
- 基于KDE的连续概率接触检测自适应缩放ZUPT因子协方差。
- 四足机器人非结构地形实验显示其轨迹更准、垂直发散更小、接触过渡更平滑。

**验证范围与局限**：验证范围为四足机器人在多种非结构化地形上的实验。

## 英文摘要 (Abstract)

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.

作者：Yicheng Zhou, Longwen Chen, Qinghua Yu, Junhao Xiao, Huimin Lu, Xieyuanli Chen
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## 原始参考来源 (Sources)

- [arXiv 2610.17870：LAG-IO: Multi-IMU State Estimation for Legged Robots Using Factor Graphs](https://arxiv.org/abs/2610.17870)