文献类型：期刊文献

英文题名：An Integrated Visual Odometry System With Stereo Camera for Unmanned Underwater Vehicles

作者：Xu, Zhizun[1];Haroutunian, Maryam[2];Murphy, Alan J.[2];Neasham, Jeff[2];Norman, Rose[2]

机构：[1]Guangdong Ocean Univ, Maritime Coll, Zhanjiang 524091, Peoples R China;[2]Newcastle Univ, Sch Engn, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England

年份：2022

卷号：10

起止页码：71329

外文期刊名：IEEE ACCESS

收录：SCI-EXPANDED(收录号：WOS:000838419000001)、、EI(收录号：20222812349866)、Scopus(收录号：2-s2.0-85133731902)、WOS

基金：The authors would like to acknowledge the generous support of John and Vivien Prime in funding aspects of this work.

语种：英文

外文关键词：Visual odometry; Cameras; Visualization; Navigation; Sonar navigation; Feature extraction; Estimation; Underwater navigation; underwater vehicles; visual-inertial odometry; sensor fusion

外文摘要：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.