Visual navigation of an autonomous underwater vehicle based on the global search of image correspondences

A task of autonomous underwater vehicle (AUV) navigation is considered in the paper. The images obtained from an onboard stereo camera are used to build point clouds attached to a particular AUV position. Quantized SIFT descriptors of points are stored in a metric tree to organize an effective search procedure using a best bin first approach. Correspondences for a new point cloud are searched in a compact group of point clouds that have the largest number of similar descriptors stored in the tree. The new point cloud can be positioned relative to the other clouds without any prior information about the AUV position and uncertainty of this position. This approach increases the reliability of the AUV navigation system and makes it insensitive to data losses, textureless seafloor regions and long passes without trajectory intersections. Several algorithms are described in the paper: an algorithm of point clouds computation, an algorithm for establishing point clouds correspondence, and an algorithm of building groups of potentially linked point clouds to speedup the global search of correspondences. The general navigation algorithm consisting of three parallel subroutines: image adding, search tree updating, and global optimization is also presented. The proposed navigation system is tested on real and synthetic data. Tests on real data showed that the trajectory can be built even for an image sequence with 60% data losses with successive images that have either small or zero overlap. Tests on synthetic data showed that the constructed trajectory is close to the true one even for long missions. The average speed of image processing by the proposed navigation system is about 3 frames per second with a middle-price desktop CPU.