This paper addresses the problem of estimating the 3D trajectory and associated uncertainty of an underwater autonomous vehicle from a set of images of the seabed taken by an onboard camera. The presented algorithms resort to the use of video mosaics and build upon previous work on image registration and visual pose estimation. The pose estimation is accomplished in two steps. Firstly, a video mosaic is created automatically, covering a region of interest of the seabed. Then, after associating a 3D referential for the mosaic, the estimation of the camera position from a new view of the scene becomes possible.
The main contribution of this paper lies on the assessment of the performance of the 3D pose algorithms. In order to do this, an image sequence with available ground-truth is used for precise error measuring. A first-order error propagation analysis is presented, relating the uncertainty in the location of the match points with the uncertainty in the pose parameters. The importance of predicting the estimate uncertainty is emphasized by the fact that it can be used for comparing algorithms and for the on-line monitoring of the vehicle trajectory reconstruction quality.
Several iterative and non-iterative pose estimation methods are discussed, differing both on the criteria being minimized and on the required information about the camera intrinsic parameters. This information ranges from the full knowledge of the parameters, to the case where they are estimated using self-calibration from an image sequence under pure rotation. The implemented pose algorithms are compared for the accuracy and estimate covariance.