  In this work, we present an aided inertial navigation system for an
autonomous underwater vehicle (AUV) using an unscented Kalman filter on
manifolds (UKF-M). The inertial navigation estimate is aided by a Doppler
velocity log (DVL), depth sensor, acoustic range and, while on the surface,
GPS. The sensor model for each navigation sensor on the AUV is explicitly
described, including compensation for lever arm offsets between the IMU and
each sensor. Additionally, an outlier rejection step is proposed to reject
measurement outliers that would degrade navigation performance. The UKF-M for
AUV navigation is implemented for real-time navigation on the Virginia Tech 690
AUV and validated in the field. Finally, by post-processing the navigation
sensor data, we show experimentally that the UKF-M is able to converge to the
correct heading in the presence of arbitrarily large initial heading error.
