Departmental Papers (ESE)


We report on a hybrid 12-dimensional full body state estimator for a hexapod robot executing a jogging gait in steady state on level terrain with regularly alternating ground contact and aerial phases of motion. We use a repeating sequence of continuous time dynamical models that are switched in and out of an extended Kalman filter to fuse measurements from a novel leg pose sensor and inertial sensors. Our inertial measurement unit supplements the traditionally paired three-axis rate gyro and three-axis accelerometer with a set of three additional three-axis accelerometer suites, thereby providing additional angular acceleration measurement, avoiding the need for localization of the accelerometer at the center of mass on the robot’s body, and simplifying installation and calibration. We implement this estimation procedure offline, using data extracted from numerous repeated runs of the hexapod robot RHex (bearing the appropriate sensor suite) and evaluate its performance with reference to a visual ground-truth measurement system, comparing as well the relative performance of different fusion approaches implemented via different model sequences.

Document Type

Journal Article

Subject Area

GRASP, Kodlab

Date of this Version

October 2006


Copyright IEEE 2006. Reprinted from IEEE Transactions on Robotics, Volume 22, Issue 5, October 2006, pages 932-943.

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extended kalman filter (ekf), hybrid estimation model, inertial measurement unit (imu), legged robot, leg pose sensor (lps), sensor fusion



Date Posted: 20 November 2006

This document has been peer reviewed.