基于虚拟相机的位姿估计研究进展  

Research Progresses of Pose Estimation Based on Virtual Cameras

作　　者：李安虎[1] 邓兆军 刘兴盛 陈昊[1] Li Anhu;Deng Zhaojun;Liu Xingsheng;Chen Hao(School of Mechanical Engineering,Tongji University,Shanghai 201804,China)

机构地区：[1]同济大学机械与能源工程学院,上海201804

出　　处：《激光与光电子学进展》2022年第14期19-33,共15页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(61975152)。

摘　　要：物体位姿估计在机器人、无人驾驶、航空航天、虚拟现实等领域有着广泛的应用。主要围绕主流测量系统及方法展开论述,对比分析各类测量系统和方法的差异、优势与缺陷,综述其研究现状、前沿动向和热点问题。相比之下,基于虚拟相机的位姿估计系统具有突出的系统集成度,同时兼顾高精度和低成本;基于深度学习的方法在场景和目标适应性方面表现突出,有望进一步广泛应用于工业场景。最后,从感知场景和对象出发,分析当前位姿估计技术面临的诸多严峻挑战,展望位姿估计技术的研究重点和方向。Pose estimation for objects is employed in rich artificial intelligence fields,such as robotics,unmanned driving,aerospace,and virtual reality.This paper mainly discusses the mainstream measurement systems and methods in term of research status,frontier trends,and hot issues,the differences,advantages and disadvantages of which are compared and analyzed in detailed.In general,virtual-camera-based pose estimation systems have outstanding system integration while providing with high accuracy and low cost.Deep-learning-based methods exhibit excellent performance in adaptability of scenes and objects,which are expected to be widely used in unstructured industrial scenarios.Finally,starting from the perception of scenes and objects,this paper analyzes many severe challenges faced by the current pose estimation technology,and looks forward to the research focus and direction of pose estimation technology.

关 键 词：仪器测量与计量 位姿估计 虚拟相机 深度学习 

分 类 号：TH86[机械工程—仪器科学与技术]