计算机应用研究2024，Vol.41Issue(7)：2209-2214,6.DOI:10.19734/j.issn.1001-3695.2023.09.0512
一种在线更新的单目视觉里程计
Online-updating monocular visual odometry
摘要
Abstract
When training samples of existing deep learning-based visual odometry(VO)are different from application scena-rios,it is difficult to adapt to the new environment.Therefore,this paper proposed an online updated monocular visual mileage calculation method(OUMVO).In the application stage,it optimized the pose estimation network model online by using the real-time image sequence,which improved the generalization ability of the network and the ability to apply to the new environ-ment.At the same time,it utilized self-supervised learning method without the need to mark the ground truth.Moreover,it adopted Transformer to conduct sequential modeling of image streams to make full use of the visual information within the local window to improve the precision of the pose estimation in order to avoid the limitation that the traditional method could only use two adjacent frames to estimate the pose.It could also compensate for the shortcomings of using RNN for sequence modeling which could not be calculated in parallel.In addition,it used the geometric consistency constraint of the image space to solve the scale drift problem of the traditional monocular visual mileage calculation method.Quantitative and qualitative experimen-tal results on the KITTI dataset show that the proposed method is superior to existing state-of-the-art monocular visual odometry methods in terms of pose estimation accuracy and adaptability to new environments.关键词
视觉里程计/单目视觉/在线更新/自监督学习/Transformer神经网络Key words
visual odometry/monocular visual/online update/self-supervised deep learning/Transformer neural network分类
信息技术与安全科学引用本文复制引用
王铭敏,佃松宜,钟羽中..一种在线更新的单目视觉里程计[J].计算机应用研究,2024,41(7):2209-2214,6.基金项目
国家重点研发计划资助项目(2018YFB1307402) （2018YFB1307402）
