A Neural Network-Based Camera Calibration Method for Mobile Robot Localization Problems

Zou, An‐Min; Hou, Zeng-Guang; Zhang, Lejie; Tan, Min

doi:10.1007/11427469_44

Cited by 9 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The projection or back-projection function has also been modeled and computed using neural networks, see for example [9,7,535,577]. This is an interesting approach since it is inherently flexible and can be applied to calibrate various kinds of cameras with various types of distortion.…”

Section: Neural Networkmentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

View full text Add to dashboard Cite

A printed and bound version is available at a special discount price of US35 from Now Publishers. This can be obtained by entering the promotional code CGV006023 on https://www.nowpublishers.com/bookorder.aspx?doi=0600000023&product=CGVInternational audienceThis survey is mainly motivated by the increased availability and use of panoramic image acquisition devices, in computer vision and various of its applications. Different technologies and different computational models thereof exist and algorithms and theoretical studies for geometric computer vision ("structure-from-motion") are often re-developed without highlighting common underlying principles. One of the goals of this survey is to give an overview of image acquisition methods used in computer vision and especially, of the vast number of camera models that have been proposed and investigated over the years, where we try to point out similarities between different models. Results on epipolar and multi-view geometry for different camera models are reviewed as well as various calibration and self-calibration approaches, with an emphasis on non-perspective cameras. We finally describe what we consider are fundamental building blocks for geometric computer vision or structure-from-motion: epipolar geometry, pose and motion estimation, 3D scene modeling, and bundle adjustment. The main goal here is to highlight the main principles of these, which are independent of specific camera models

show abstract

Section: Neural Networkmentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

View full text Add to dashboard Cite

show abstract

“…10, for our developed mobile robot using the proposed neural units with higher-order synaptic operations. We are also developing dynamic neural units for wider applications in the field of information processing and control for robotics [10,25,26].…”

Section: Edge Detection Using Neural Units With Higher-order Synapticmentioning

confidence: 99%

Neural Units with Higher-Order Synaptic Operations for Robotic Image Processing Applications

et al. 2006

Self Cite

View full text Add to dashboard Cite

Neural units with higher-order synaptic operations have good computational properties in information processing and control applications. This paper presents neural units with higher-order synaptic operations for visual image processing applications. We use the neural units with higherorder synaptic operations for edge detection and employ the Hough transform to process the edge detection results. The edge detection method based on the neural unit with higherorder synaptic operations has been applied to solve routing problems of mobile robots. Simulation results show that the proposed neural units with higher-order synaptic operations are efficient for image processing and routing applications of mobile robots.

show abstract

“…Landmark-based localization method is most commonly used in which landmark recognition plays an important role. In recent years, monocular vision-based localization method has been widely used since we can obtain abundant information from visual sensors [1,2,3,4]. In 1988, Sugihara presented one of the pioneering studies in the landmark-based localization using monocular vision [1].…”

Section: Introductionmentioning

confidence: 99%

“…In [3], the authors have analyzed the effects of false positive (observed landmark that do not correspond to a known landmark). In our previous work [4], we presented a neural network based camera calibration method for the global localization of mobile robots using monocular vision. We used the camera to measure the relative location between the floor landmark and the robot, whereas in [1,2,3], the camera was used to measure the bearing of one landmark relative another.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machines (SVM) for Color Image Segmentation with Applications to Mobile Robot Localization Problems

Zou

Hou

Tan

2005

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. In autonomous mobile robot industry, the landmark-based localization method is widely used in which the landmark recognition plays an important role. The landmark recognition using visual sensors relies heavily on the quality of the image segmentation. In this paper, we use seat numbers as the landmarks, and it is of great importance to the seat number recognition that correctly segment the number regions from images. To perform this assignment, the support vector machine method is adopted to solve the color image segmentation problems because of its good generalization ability. The proposed method has been used for the mobile robot localization problems, and experimental results show that the proposed method can bring robust performance in practice.

show abstract

A Neural Network-Based Camera Calibration Method for Mobile Robot Localization Problems

Cited by 9 publications

References 14 publications

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Neural Units with Higher-Order Synaptic Operations for Robotic Image Processing Applications

Support Vector Machines (SVM) for Color Image Segmentation with Applications to Mobile Robot Localization Problems

Contact Info

Product

Resources

About