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椭圆形视觉标志物检测及其在视觉定位中的应用
钱正达
2022-05-24
Pages64
Subtype硕士
Abstract

视觉定位,即是根据图像信息计算出相机在三维空间中的位置和姿态,在虚
拟现实 (VR,Virtual Reality),增强现实 (AR,Augmented Reality),机器人定位
与导航,无人驾驶等领域有着广泛而重要的应用。然而,在弱纹理、运动模糊、
光照变化等条件下,视觉定位的精度和鲁棒性较低,借用标志物来进行辅助定位
是一种较好的解决方案。圆形标志物在日常生活中较常见并且具有较高的可识
别性。因此,本论文围绕圆形标志物的图像检测与定位展开研究。论文的贡献如
下:
1. 提出了一种基于旋转范围框的图像椭圆检测器 EllipseNet。圆形标志物在
二维图像上的投影通常为椭圆形,利用搭建的神经网络检测器可提取椭圆标志
物的区域,并能对任意方向的椭圆做出角度预测,实验表明该椭圆标志物检测器
的准确率比基于正框的检测器准确率更高。
2. 提出了用于椭圆标志物检测的数据库构建方法以及 EllipseNet 的训练方
法。针对深度学习在训练阶段需求大量训练数据以及椭圆参数标注困难等问题,
采用单应变换、背景替换、光照变化自动生成大量数据。同时,该方法减少了人
工标注范围框所引入的误差。所提出的训练函数同时考虑了旋转范围框的中心
点和形状,使得椭圆标志物检测更加精确。
3. 设计了一种圆形标志物并基于 EllipseNet 检测器进行相机定位。采用了圆
形的射影不变性,给出了相机位姿的解析解。实验表明该方法与常用的方形标志
物相比,在远距离、光照变化、视角变化上具有更高的鲁棒性。

Other Abstract

Visual localization is to estimate the position and rotation of a camera in three-
dimensional space from images, which has wide and important applications in virtual
reality (VR), augmented reality(AR), robot navigation, and unmanned driving. How-
ever, under the conditions of weak textures, motion blur and illumination changes, the
accuracy and robustness of visual localization are not high. Visual localization with
markers is a good solving scheme. Circular markers are common in daily life and have
high discriminative feature. This paper focuses on visual ellipse region detection and its
application on visual localization from circle markers. The contributions of the paper
are as follows:
1. An image ellipse region detector based on a rotating bounding box is proposed,
named EllipseNet. Generally, projections of circular markers in images are elliptical
shapes. The detector can extract the region of the ellipse marker and predict the angle
of the ellipse in any direction. Experiments demonstrate that accuracy of the detector is
higher than those with upright bounding boxes.
2. An image dataset of circle markers is constructed automatically and training
manners of EllipseNet on the dataset are given. This construction method can auto-
matically generate a large amount of data for deep learning. Thus, EllipseNet can be
trained without manual annotations. At the same time, this dataset generating method
reduces the error caused by manual annotations. The proposed training loss function
takes account of the center and shape of the rotating bounding box, making the ellipse
region detection more accurate.
3. A circular marker is designed and camera localization is carried out based on
EllipseNet detector. Using the circular projective invariance, the analytical solution of
the camera pose is given. Experiments show that the proposed method is more robust to
far distance, illumination and viewpoint changes than the commonly used rectangular
markers.

Keyword目标检测 椭圆检测 位姿估计 数据增强
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48485
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
钱正达. 椭圆形视觉标志物检测及其在视觉定位中的应用[D]. 自动化研究所. 自动化研究所,2022.
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