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Implementing dynamic locomotion behaviors on legged robots requires ahigh-quality state estimation module. Especially when the motion includesflight phases, state-of-the-art approaches fail to produce reliable estimationof the robot posture, in particular base height. In this paper, we propose anovel approach for combining visual-inertial odometry (VIO) with leg odometryin an extended Kalman filter (EKF) based state estimator. The VIO module uses astereo camera and IMU to yield low-drift 3D position and yaw orientation anddrift-free pitch and roll orientation of the robot base link in the inertialframe. However, these values have a considerable amount of latency due to imageprocessing and optimization, while the rate of update is quite low which is notsuitable for low-level control. To reduce the latency, we predict the VIO stateestimate at the rate of the IMU measurements of the VIO sensor. The EKF moduleuses the base pose and linear velocity predicted by VIO, fuses them furtherwith a second high-rate IMU and leg odometry measurements, and produces robotstate estimates with a high frequency and small latency suitable for control.We integrate this lightweight estimation framework with a nonlinear modelpredictive controller and show successful implementation of a set of agilelocomotion behaviors, including trotting and jumping at varying horizontalspeeds, on a torque-controlled quadruped robot.

Visual-Inertial and Leg Odometry Fusion for Dynamic Locomotion