Augmented State Kalman Filtering for AUV Navigation

This paper addresses the problem of estimating the motion of an Autonomous Underwater Vehicle (AUV), while it constructs a visual map (“mosaic ” image) of the ocean jlool: The vehicle is equipped with a down-looking camera which is used to compute its motion with respect to the seaflool: As the mosaic increases in sue, a systematic bias is introduced in the alignment of the images which form the mosaic. Therefore, this accumulative error produces a dr @ in the estimation of the position of the vehicle. When the arbitrary trajectory of the AUV crosses over itself; it is possible to reduce this propagation of image alignment errors within the mosaic. A Kalman filter with augmented state is proposed to optimally estimate both the visual map and the vehicle position,