State estimation and vehicle localization for the FIDO rover

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Abstract
This paper describes the means for generating rover localization information for NASA/JPL's FIDO rover. This is accomplished using a sensor fusion framework which combines wheel odometry with sun sensor and inertial navigation sensors to provide an integrated state estimate for the vehicle's position and orientation relative to a fixed reference frame. This paper describes two separate state estimation approaches built around the extended Kalman filter formulation and the Covariance Intersection formulation. Experimental results from runs in JPL's MarsYard are presented in order to compare the state estimates generated using each formulation.

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