LAUSR

Navigation is a challenging problem in the area of underwater unmanned vehicles, due to the significant electronmagnetic wave attenuation and the uncertainties in underwater environments. The conventional methods, mainly implemented by acoustic devices, suffer limitations such as high cost, terrain effects and low refresh rate. In this paper, a novel low-cost underwater visual navigation method, named Integrated Visual Odometry with a Stereo Camera (IVO-S), has been investigated. Unlike pure visual odometry, the proposed method fuses the information from inertial sensors and a sonar so that it is able to work in context-sparse environments. In practical experiments, the vehicle was operated to follow specific closed-loop shapes. The Integrated Visual Odoemtry with Monocular Camera (IVO-M) method and other popular open source Visual SLAMs (Simultaneous Localisation and Mappings), such as ORB-SLAM2 and VINS-Mono, have been used to provide comparative results. The cumulative error ratio is used as the quantitative evaluation method to analyse the practical test results. It is shown that the IVO-S method is able to work in underwater sparse-feature environments with high accuracy, whilst also being a low cost solution.