摘要
为了提高移动机器人的定位精度,提出一种双目视觉与惯导融合的视觉SLAM算法。在视觉SLAM前端部分,为了保持直接法计算速度快及特征法精度高的特点,提出一种融合直接法和特征法的半直接法双目视觉里程计。在后端优化阶段,将视觉数据与IMU数据相互融合,在滑动窗口中以非线性优化的方式构建误差函数,优化位姿计算精度。在EuRoc数据集中对本文提出的算法进行试验验证。结果表明,与开源的视觉惯导融合的SLAM系统OKVIS、ROVIO和VINS-Mono相比,本文系统在Machine Hall与Vicon Room两个场景中的定位精度均得到了明显的提升,同时可以保持较高的运行效率。
To improve the positioning accuracy of mobile robot,a visual SLAM algorithm based on binocular vision and inertial navigation is presented.In the front part of visual SLAM,a semi-direct binocular visual odometer combining direct method with characteristic method is presented to maintain the fast calculation speed and high accuracy of direct method.In the back-end optimization stage,the visual data and IMU data are fused together,and error functions are constructed in a sliding window in a non-linear optimization way to optimize the accuracy of pose calculation.The algorithm proposed in this paper is validated in EuRoc dataset.The results show that the positioning accuracy of the SLAM system OKVIS,ROVIO and VINS-Mono is significantly improved in both Machine Hall and Vicon Room scenarios,while maintaining high operational efficiency,compared with the open source visual inertial navigation fusion SLAM system OKVIS,ROVIO and VINS-Mono.
作者
许智宾
李宏伟
张斌
肖志远
邓晨
XU Zhibin;LI Hongwei;ZHANG Bin;XIAO Zhiyuan;DENG Chen(School of Information Engineering, Zhengzhou University, Zhengzhou 450052, China;School of Geoscience and Technology, Zhengzhou University,Zhengzhou 450052, China;School of Water Conservancy Science and Engineering,Zhengzhou University, Zhengzhou 450001, China)
出处
《测绘学报》
EI
CSCD
北大核心
2021年第11期1512-1521,共10页
Acta Geodaetica et Cartographica Sinica
基金
中国工程科技发展战略河南研究院战略咨询研究项目(2020HENZT07)。
