摘要
针对水下视觉导航在弱纹理环境下定位精度低及稳健性较差的问题,本文提出了一种基于稀疏直接法的水下单目视觉惯性里程计。该方法基于像素灰度不变的假设,通过优化光度误差估计相机位姿,避免了特征点提取和匹配的复杂过程,从而提高了导航的实时性和稳健性;同时,结合惯性测量单元(IMU)的数据,利用误差状态卡尔曼滤波(ESKF)进行数据融合进一步减小误差,以提高自主水下机器人(AUV)在水下复杂环境导航的稳定性和精度。试验结果表明,误差达厘米级且与单纯的视觉算法相比,有所减小,证明了该系统能够有效融合视觉和惯性信息,在水下导航领域具有较高的精度和稳健性。
Aiming at the problems of low localization accuracy as well as poor robustness of underwater visual navigation in weak texture environments,this paper proposes an underwater monocular visual inertial odometry based on the sparse direct method.The method is based on the assumption of pixel gray scale invariance,and estimates the camera position by optimizing the photometric error,avoids the complex process of feature point extraction and matching,thus improves the real-time and robustness of navigation,while combines the data from the inertial measurement unit(IMU)and uses error state Kalman filter(ESKF)for data fusion to further reduce the error,in order to improve the stability of navigation of autonomous underwater vehicle(AUV)in underwater complex environments.The stability and accuracy of navigation in underwater complex environments are improved.The experimental results show that the error reaches the centimeter level and is reduced compared with the vision-only algorithm,which proves that the system can effectively fuse vision and inertial information,and has high accuracy and robustness in the field of underwater navigation.
作者
王益美
黄琰
冯浩
WANG Yimei;HUANG Yan;FENG Hao(State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110169,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《测绘通报》
北大核心
2025年第1期94-100,共7页
Bulletin of Surveying and Mapping
基金
机器人学国家重点实验室自主课题(E21Z0703)。
关键词
稀疏直接法
自主水下机器人
惯性测量单元
视觉惯性里程计
误差状态卡尔曼滤波
sparse direct method
autonomous underwater vehicle
inertial measurement unit
visual inertial odometry
error state Kalman filter
