Kalman filtering of IMU sensor for robot balance control

Abstract

This study explores the use of Kalman filtering of measurements from an inertial measurement unit (IMU) to provide information on the orientation of a robot for balance control. A test bed was created to characterize the random noise and errors inherent to orientation sensing in the MicroStrain 3DM-GX1 IMU for static cases as well as after experiencing an impact force. Balance simulations were performed to control the center of mass location of a robot modeled as an inverted pendulum. The controlled center of mass trajectories with state estimates generated from Kalman filtering were compared, where possible, to the CM trajectory based on unfiltered sensor measurements of the states. For the simple case of inverted pendulum control, it was determined that noise and error in the IMU are sufficiently small that Kalman filtering is not necessary when all states can be measured, but results in significant improvements in the RMS error of the actual and desired center of mass positions.