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This paper presents a manifold based Unscented Kalman Filter that applies anovel strategy for inertial, model-aiding and Acoustic Doppler Current Profiler(ADCP) measurement incorporation. The filter is capable of observing andutilizing the Earth rotation for heading estimation with a tactical grade IMU,and utilizes information from the vehicle model during DVL drop outs. The dragand thrust model-aiding accounts for the correlated nature of vehicle modelparameter error by applying them as states in the filter. ADCP-aiding providesfurther information for the model-aiding in the case of DVL bottom-lock loss.Additionally this work was implemented using the MTK and ROCK framework in C++,and is capable of running in real-time on computing available on the FlatFishAUV. The IMU biases are estimated in a fully coupled approach in the navigationfilter. Heading convergence is shown on a real-world data set. Furtherexperiments show that the filter is capable of consistent positioning, and datadenial validates the method for DVL dropouts due to very low or high altitudescenarios.

Robust Model-Aided Inertial Localization for Autonomous Underwater Vehicles