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Published in: Neural Computing and Applications 7/2019

26-10-2017 | Original Article

Automatic single-view monocular camera calibration-based object manipulation using novel dexterous multi-fingered delta robot

Authors: Sachin Kansal, Sudipto Mukherjee

Published in: Neural Computing and Applications | Issue 7/2019

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Abstract

The approximation of 3D geometry through single image is a particular instance of 3D reconstruction from several images. The advance information or user input should be provided to retrieve or conclude depth information. This research presents a novel automatic method to enumerated 3D affine measurements from a single perspective image. The least geometric information has been resolute through the image of the scene. The vanishing line and a vanishing point are two required information to reconstruct from an image of a scene. The affine scene structure can be reconstructed through the image of a scene. The proposed approach has many advantages; there is no need of the camera’s intrinsic matrix and the explicit correlation among camera and scene (pose), no need for selecting Vx, Vy, Vz points, novel dexterous robot architecture for manipulation. In this paper, the following approaches have been implemented: (1) the three sets of vanishing points in X, Y, and Z axis; (2) the vanishing lines of the image; (3) distance among planes that parallel to the reference plane; (4) image wrapping; (5) corner detection (algorithm has been implemented in order to make the process automatic). The indigenous data set has been taken for the experiment. The results are compared with Zhang- and ArUco-based calibration. This novel approach has been used to perform tracking and manipulation of an object in real-time environment.

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Metadata
Title
Automatic single-view monocular camera calibration-based object manipulation using novel dexterous multi-fingered delta robot
Authors
Sachin Kansal
Sudipto Mukherjee
Publication date
26-10-2017
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 7/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3221-3

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