Extended Kalman filter for frequent local and infrequent global sensor data fusion

Details Extended Kalman filter for frequent local and infrequent global sensor data fusion Extended Kalman filter for frequent local and infrequent global sensor data fusion

Sep 1997

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997spie.3209...11r&link_type=abstract

Proc. SPIE Vol. 3209, p. 11-22, Sensor Fusion and Decentralized Control in Autonomous Robotic Systems, Paul S. Schenker; Gerard

Computer Science

Performance

Scientific paper

In this paper we compare the performance of a dead-reckoning system for robot navigation to a system using an extended Kalman filter (EKF). Dead-reckoning systems are able to approximate position and orientation by feeding data (provided usually by local sensors) to the kinematic model of the vehicle. These systems are subject to many different sources of error. EKFs have the ability to combine the same information and compensate for most of these errors to yield a better estimate. Our simulation results using a simplified kinematic model of Rocky 7 [an experimental rover used in the Mars exploration program at Jet Propulsion Laboratory (JPL)] show that an improvement in performance up to 40% (position error) can be achieved. The local sensors used are: wheel encoders, steering angle potentiometer and gyroscope. Involvement of global sensor measurements can drastically increase the accuracy of the estimate. The lack of GPS or magnetic field on Mars narrows our choices for global localization. Landmarks, such as the sun can be used as natural beacons (reference point for absolute measuremnts). A sun sensor (SS) that measures the absolute orientation of the rover has been built by Lockheed Martin and now is part of the sensor suite of Rocky 7. The SS measurement is crucial for the estimation filter and we show that the accuracy of the estimation decreases exponentially as the frequency of the SS data fed to the EKF decreases.

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