一种摄像机自运动估计和场景结构同步恢复的视觉惯性方法

2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems Pub Date : 2010-10-25 DOI:10.1109/VECIMS.2010.5609344

Dominik Aufderheide, W. Krybus

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引用次数: 5

摘要

在增强现实(AR)、视觉导航、机器人或娱乐等许多不同的应用领域，对相机自我运动的估计是一个非常理想的目标。特别是在实时建模中，摄像机轨迹的预估是生成三维场景模型的基本步骤。本文提出了一种结合视觉和惯性线索同时恢复场景结构和摄像机运动的框架。为此，提出了两种不同的系统设计:松散耦合系统和单片设计，该设计将非线性状态估计的思想作为扩展卡尔曼滤波(EKF)用于结构和运动恢复。

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A visual-inertial approach for camera egomotion estimation and simultaneous recovery of scene structure

The estimation of a camera's egomotion is a highly desireable goal in many different application fields such as augmented reality (AR), visual navigation, robotics or entertainment. Especially for real-time modeling the former estimation of the camera trajectory is an elementary step towards the generation of three dimensional scene models. This paper presents a framework for simultaneous recovery of scene structure and camera motion by combining visual and inertial cues. For this purpose two different system designs are proposed: a loosely-coupled system and a monolithic design, which adapts ideas from non-linear state estimation as extended Kalman filtering (EKF) for structure and motion recovery.

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2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems

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