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Monocular 3D metric scale reconstruction using depth from defocus and image velocity

机译:使用离焦和图像速度的深度进行单眼3D度量尺度重构

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摘要

This paper presents a novel approach to metric scale reconstruction of a three-dimensional (3D) scene using a monocular camera. Using a sequence of images from a monocular camera with a fixed focus lens, metric distance to a set of features in the environment is estimated from image blur due to defocus. The blur texture ambiguity which causes scale errors in depth from defocus is corrected in an EKF framework that exploits image velocity measurements. We show in real experiments that our method converges to a metric scale, accurate, sparse depth map and 3D camera poses with images from a monocular camera. Therefore, the proposed approach has the potential to enhance robot navigation algorithms that rely on monocular cameras.
机译:本文提出了一种使用单眼相机对三维(3D)场景进行度量尺度重构的新颖方法。使用来自具有固定聚焦透镜的单眼相机的图像序列,可以根据由于散焦导致的图像模糊来估计到环境中一组特征的度量距离。在利用图像速度测量的EKF框架中纠正了由于散焦而导致深度缩放错误的模糊纹理模糊性。我们在真实的实验中显示,我们的方法可以收敛到公制比例,准确,稀疏的深度图和3D相机姿势,以及来自单眼相机的图像。因此,提出的方法具有增强依赖单眼相机的机器人导航算法的潜力。

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