3-D motion estimation for positioning from 2-D acoustic video imagery

We address the problem of estimating 3-D motion from acoustic images acquired by high-frequency 2-D imaging sonars deployed in underwater. Utilizing a planar approximation to scene surfaces, two-view homography is the basis of a nonlinear optimization method for estimating the motion parameters. There is no scale factor ambiguity, unlike the case of monocular motion vision for optical images. Experiments with real images demonstrate the potential in a range of applications, including target-based positioning in search and inspection operations.