Underwater navigation presents crucial issues because of the rapid attenuation of electronic magnetic waves. The conventional underwater navigation methods are achieved by acoustic equipment, such as the ultra-short-baseline localisation systems and Doppler velocity logs, etc. However, they suffer from low fresh rate, low bandwidth, environmental disturbance and high cost. In the paper, a novel underwater visual navigation is investigated based on the multiple ArUco markers. Unlike other underwater navigation approaches based on the artificial markers, the noise model of the pose estimation of a single marker and an optimal algorithm of the multiple markers are developed to increase the precision of the method. The experimental tests are conducted in the towing tank. The results show that the proposed method is able to localise the underwater vehicle accurately.

Underwater navigation is always a challenging problem because of electromagnetic attenuation. The traditional methods involve beacons, inertial sensors, and Doppler velocity log, but they have many shortcomings, such as high cost and lengthy setup time. In order to solve underwater navigation problems at low cost, an integrated visual odometry system has been developed and discussed in this article. In this method, two inertial sensors provide acceleration and attitude of the vehicle, and an underwater sonar is used to provide the distance between the vehicle and the seabed, whilst in the visual odometry section, an optical flow algorithm has been applied for tracking feature points. With the depth provided by the sonar, 3-D position of feature points can be calculated. Linear motion of the vehicle is then predicted through these feature points in dual frames. Finally, nonlinear optimization is used to correct the attitude of the vehicle using visual information. In the proposed algorithm, the vehicle trajectory can be estimated in absolute scale by using a single camera; computational complexity is reduced dramatically compared to other visual odometry methodologies; and this algorithm allows the approach to work in sparse texture conditions. The results from practical experiments demonstrate that the method is effective and it is also a low-cost solution.

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.