Visual Odometry and Mapping for Underwater Autonomous Vehicles

摘要

This work proposed a new approach to visual odometry and mapping of a underwater robot using only online visual information. This system can be used either in autonomous inspection tasks or in control assistance of robot closed-loop, in case of a human remote operator. A set of tests were performed under different underwater conditions. The effectiveness of our proposal was evaluated inside a set of real scenario, with different levels of turbidity, snow marine, non-uniform illumination and noise, among others conditions. The results have shown the SIFT advantages in relation to others methods, as KLT, in reason of its invariance to illumination conditions and perspective transformations. The estimated localization is robust, comparing with the vehicle real pose. Considering time performance, our proposal can be used to online AUV SLAM, even in very extreme sea conditions. The correlations of interest points provided by SIFT were satisfying, even though with the presence of many outliers, i.e., false correlations. The proposal of use of fundamental matrix estimated in robust ways in order to remove outliers through RANSAC and LMedS algorithms. The original iintegration of SIFT and topological maps with GCS for AUV navigation is a promising field. The topological mapping based on Kohonen Nets and GCS showed potencial potential to underwater SLAM applications using visual information due to its robustness to sensory impreciseness and low computational cost. The GCS stabilizes in a limited number of nodes sufficient to represent a large number if descriptors in a long sequence of frames. The SOM localization shows good results, validating its use with visual odometry.