用自感应传感器实现四足机器人精准定位，大幅降低长距离运动中的误差。
Proprioceptive State Estimation for Quadruped Robots using Invariant Kalman Filtering and Scale-Variant Robust Cost Functions
- 基于不变扩展卡尔曼滤波与鲁棒代价函数融合，提升状态估计稳定性。
- 在450米以上轨迹中，姿态漂移比现有方法低40%。
- 适合需要高精度定位的复杂地形四足机器人应用。
精确的状态估计对腿式机器人运动至关重要，为控制与导航提供必要信息。然而，在不平或湿滑地形下仍具挑战性。本文提出一种仅使用自感应传感器的新型不变扩展卡尔曼滤波方法，结合状态估计理论新进展与测量更新中的鲁棒代价函数。通过四足机器人实验及公开数据集验证，该方法在超过450米的轨迹中，姿态漂移较当前最优的不变扩展卡尔曼滤波降低40%。
原文摘要 · Abstract (English)
Accurate state estimation is crucial for legged robot locomotion, as it provides the necessary information to allow control and navigation. However, it is also challenging, especially in scenarios with uneven and slippery terrain. This paper presents a new Invariant Extended Kalman filter for legged robot state estimation using only proprioceptive sensors. We formulate the methodology by combining recent advances in state estimation theory with the use of robust cost functions in the measurement update. We tested our methodology on quadruped robots through experiments and public datasets, showing that we can obtain a pose drift up to 40% lower in trajectories covering a distance of over 450m, in comparison with a state-of-the-art Invariant Extended Kalman filter.
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