State estimation for legged locomotion over a dynamic rigid surface (DRS), which is a rigid surface moving in the world frame (e.g., ships, aircraft, and trains), remains an under-explored problem. This paper introduces an invariant extended Kalman filter that estimates the robot's pose and velocity during DRS locomotion by using common sensors of legged robots (e.g., inertial measurement units (IMU), joint encoders, and RDB-D camera). A key feature of the filter lies in that it explicitly addresses the nonstationary surface-foot contact point and the hybrid robot behaviors. Another key feature is that, in the absence of IMU biases, the filter satisfies the attractive group affine and invariant observation conditions, and is thus provably convergent for the deterministic continuous phases. The observability analysis is performed to reveal the effects of DRS movement on the state observability, and the convergence property of the hybrid, deterministic filter system is examined for the observable state variables. Experiments of a Digit humanoid robot walking on a pitching treadmill validate the effectiveness of the proposed filter under large estimation errors and moderate DRS movement. The video of the experiments can be found at: https://youtu.be/ScQIBFUSKzo.
翻译:在动态硬质表面(如船舶、飞机和火车)上,国家测测得的脚部移动速度,这是一个在世界框架中(如船舶、飞机和火车等)的硬质表面移动,这仍然是一个探索不足的问题。本文介绍了一个无差别的扩大的卡尔曼过滤器,该过滤器通过使用常见的脚部机器人传感器(如惯性测量单位、联合编码器和RDB-D照相机)来估计机器人移动期间机器人的姿势和速度。过滤器的一个关键特征在于它明确针对非静止表面-脚接触点和混合机器人行为。另一个关键特征是,在没有IMU偏向的情况下,过滤器满足有吸引力的人群的松动和不灵活观察条件,因此对确定性连续阶段具有可调合性。进行可视性分析是为了揭示DRS运动对国家可观测性的影响以及混合、确定性过滤系统特性。在投影的图像中度人类机器人在投影机/KFI上行走的实验:在移动的磁性变率中,可以验证拟议的磁性测试。