Autonomous Navigation for Adaptive Unmanned Underwater Vehicles Using Fiducial Markers

Abstract

This paper presents an integrated methodology and experimental validation of an autonomous framework for unmanned underwater vehicles (UUVs) merely equipped with a conventional monocular camera and a pressure sensor to accomplish high-performance autonomy. Optimal pose of the UUV is solved iteratively by Levenburg-Marquardt optimization for the Perspective-n-Point (PnP) problem. To guarantee a consistent localization system, a properly-tuned EKF with extra outlier removal approaches including applying Chi-square tests of innovations adequately removes measurement noises, mean-while provides unknown navigation state estimations. A classic adaptive controller is developed to enable autonomous mobility. Real-time experiments are designed to demonstrate underwater autonomous performance with a miniature commercial UUV, BlueROV2 Heavy.