用足部接触信息校正机器人惯性传感器漂移，提升定位精度。
Four Simple Proprioceptive Estimators for Legged Robots
- 基于足部接触建模，逐步增强状态估计方法表达能力。
- 在真实数据上实现厘米级定位误差，显著优于纯惯性方案。
- 适合研究足式机器人自主导航与姿态估计算法的开发者。
足式机器人配备惯性测量单元（IMU），但消费级IMU噪声大，导致惯性解算存在漂移。然而，足部与环境的间歇性接触可用来缓解这一问题。本文提出一系列逐步增强的机器人状态估计算法，均以浮点基状态（姿态、位置、速度及IMU偏置）为表示。首先采用Hartley等人提出的接触辅助不变扩展卡尔曼滤波器（contact-aided invariant EKF），但降低接触更新频率；随后引入小型因子图替代测量更新；最后将相同因子构建为带接触事件的固定滞后平滑器，支持时变或恒定的IMU偏置。为促进复现与后续研究，所有四个版本均已集成至GTSAM，并提供ROS2兼容实现。
原文摘要 · Abstract (English)
Legged robots carry an IMU, but the inertial solution drifts because consumer-grade IMUs are noisy. However, the feet create intermittent contacts with the environment that can be used to mitigate that drift. This report develops a sequence of increasingly expressive legged robot state estimators that leverage this. In all cases, the floating-base state comprises attitude, position, velocity, and IMU biases. To model foot contacts, we start from the contact-aided invariant EKF of Hartley et al., albeit at a reduced contact update rate. This is then augmented by replacing the measurement update by a small factor graph. Finally, we turn the same factors into a fixed-lag smoother with contact-episode footholds, with and without an evolving IMU bias. To facilitate reproducibility and further research in proprioceptive legged odometry, all four variants are available in GTSAM (Dellaert et. al), and we additionally provide a ROS2-compatible implementation.
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