Implementing dynamic locomotion behaviors on legged robots requires a high-quality state estimation module. Especially when the motion includes flight phases, state-of-the-art approaches fail to produce reliable estimation of the robot posture, in particular base height. In this paper, we propose a novel approach for combining visual-inertial odometry (VIO) with leg odometry in an extended Kalman filter (EKF) based state estimator. The VIO module uses a stereo camera and IMU to yield low-drift 3D position and yaw orientation and drift-free pitch and roll orientation of the robot base link in the inertial frame. However, these values have a considerable amount of latency due to image processing and optimization, while the rate of update is quite low which is not suitable for low-level control. To reduce the latency, we predict the VIO state estimate at the rate of the IMU measurements of the VIO sensor. The EKF module uses the base pose and linear velocity predicted by VIO, fuses them further with a second high-rate IMU and leg odometry measurements, and produces robot state estimates with a high frequency and small latency suitable for control. We integrate this lightweight estimation framework with a nonlinear model predictive controller and show successful implementation of a set of agile locomotion behaviors, including trotting and jumping at varying horizontal speeds, on a torque-controlled quadruped robot.
翻译:在腿上机器人上实施动态动动动动动动动动动动作需要高质量的状态估计模块。尤其是当运动包括飞行阶段时,最先进的方法无法产生可靠的机器人态势估计,特别是基高度。在本文中,我们提出一种新颖的方法,将视觉-内皮odology (VIO) 与腿奥氏度测定法结合到一个基于Kalman过滤器(EKF)的州测深仪中。VIO模块使用立体相机和IMU,以产生低驾驶3D定位和亚湿方向,以及惯性框架机器人基链接的无漂移定位和滚动方向。然而,这些值由于图像处理和优化而有大量的延缓度,而更新率非常低,不适合低级别的控制。为了降低悬浮性,我们预测VIO状态估算值,以IMU传感器的测量速率。EKF模块使用VIO预测的基面和直线速度,进一步将它们与第二个高调的IMU和腿的轨道定位定位定位定位定位定位定位和滚动方向连接起来。这些值由于图像处理和脚部的定位,更新速度测量度测量测量测量测量测量测量测量结果,并制作出一个高频率和高的机器人状态预测,并制作一个高压的精确度的精确度预测,包括一个高压的精确度的预测。