Using augmented state Kalman filter to localize multi autonomous underwater vehicles

the present paper describes a system for the construction of visual maps ("mosaics") and motion estimation for a set of auvs (autonomous underwater vehicles). robots are equipped with down-looking camera which is used to estimate their motion with respect to the seafloor and built an online mosaic. as the mosaic increases in size, a systematic bias is introduced in its alignment, resulting in an erroneous output. the theoretical concepts associated with the use of an augmented state kalman filter (askf) were applied to optimally estimate both visual map and the fleet position.