基于视觉的相机位姿估计方法综述  

Overview of vision-based camera pose estimation methods

作　　者：王静[1] 王一博 郭铖 郭苹 叶星 邢淑军 Wang Jing;Wang Yibo;Guo Cheng;Guo Ping;Ye Xing;Xing Shujun(College of Communication&Information Technology,Xi’an University of Science&Technology,Xi’an 710054,China)

机构地区：[1]西安科技大学通信与信息工程学院,西安710054

出　　处：《计算机应用研究》2024年第8期2241-2251,共11页Application Research of Computers

基　　金：国家自然科学基金资助项目(61701393)。

摘　　要：相机位姿估计是通过估计相机的位置坐标和环绕三个坐标轴的角度偏转,来描述其相对于给定场景的方向和位置,是自动驾驶、机器人技术等任务的重要组成部分。为帮助研究人员在相机位姿估计领域的研究,对相机位姿估计的研究现状和最新进展进行梳理。首先介绍了相机位姿估计的基本原理、评价指标和相关数据集;然后从场景关系搭建和相机姿态解算两个关键技术出发,对两阶段模型结构方法和单通道模型结构方法进行阐述总结,分别从核心算法和利用的场景信息不同上进行分类归纳分析,并对室内室外公开数据集上的表现作对比;最后阐述了该领域当前面对的挑战和未来的发展趋势。Camera pose estimation plays a crucial role in tasks such as autonomous driving and robotics,elucidating the direction and position of the camera in relation to a given scene through the estimation of its positional coordinates and angular deviations around the three coordinate axes.To facilitate the understanding of researchers in the realm of camera pose estimation,this paper comprehensively reviewed the current research status and latest progress in this field will.Firstly,it introduced the fundamental principles,evaluation indicators,and pertinent datasets associated with camera pose estimation.Subsequently,the review elaborated and summarized the two-stage model structure method and single-channel model structure method from the two key technologies of scene relationship construction and camera pose calculation.It conducted classification and analysis based on the diverse core algorithms and scene information employed,with performance comparisons drawn from indoor and outdoor public datasets.Lastly,it expounded the current challenges in the field and future development trends.

关 键 词：相机位姿估计 深度学习 场景关系搭建 姿态解算 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]