Mr. Ali Hosseininaveh Ahmadabadian Profile

Ali Hosseininaveh Ahmadabadian

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Proceedings Article | 23 May 2013 Paper

A webcam photogrammetric method for robot calibration

Ben Sargeant, Ali Hosseininaveh A., Tohid Erfani, Stuart Robson, Jan Boehm

Proceedings Volume 8791, 879103 (2013) https://doi.org/10.1117/12.2020484

KEYWORDS: Cameras, Calibration, Kinematics, Particle swarm optimization, Photogrammetry, Particles, Matrices, Data modeling, Imaging systems, Laser scanners

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This paper describes a strategy for accurate robot calibration using close range photogrammetry. A 5-DoF robot has been designed for placement of two web cameras relative to an object. To ensure correct camera positioning, the robot is calibrated using the following strategy. First, a Denavit-Hartenberg method is used to generate a general kinematic robot model. A set of reference frames are defined relative to each joint and each of the cameras, transformation matrices are then produced to represent change in position and orientation between frames in terms of joint positions and unknown parameters. The complete model is extracted by multiplying these matrices. Second, photogrammetry is used to estimate the postures of both cameras. A set of images are captured of a calibration fixture from different robot poses. The camera postures are then estimated using bundle adjustment. Third, the kinematic parameters are estimated using weighted least squares. For each pose a set of equations are extracted from the model and the unknown parameters are estimated in an iterative procedure. Finally these values are substituted back into the original model. This final model is tested using forward kinematics by comparing the model’s predicted camera postures for given joint positions to the values obtained through photogrammetry. Inverse kinematics is performed using both least squares and particle swarm optimisation and these techniques are contrasted. Results demonstrate that this photogrammetry approach produces a reliable and accurate model of the robot that can be used with both least squares and particle swarm optimisation for robot control.

Proceedings Article | 23 May 2013 Paper

Image selection in photogrammetric multi-view stereo methods for metric and complete 3D reconstruction

Ali Hosseininaveh Ahmadabadian, Stuart Robson, Jan Boehm, Mark Shortis

Proceedings Volume 8791, 879107 (2013) https://doi.org/10.1117/12.2020472

KEYWORDS: 3D image reconstruction, 3D modeling, Cameras, Image compression, Visibility, Clouds, 3D image processing, Laser scanners, Image processing, Stereoscopic cameras

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Multi-View Stereo (MVS) as a low cost technique for precise 3D reconstruction can be a rival for laser scanners if the scale of the model is resolved. A fusion of stereo imaging equipment with photogrammetric bundle adjustment and MVS methods, known as photogrammetric MVS, can generate correctly scaled 3D models without using any known object distances. Although a huge number of stereo images (e.g. 200 high resolution images from a small object) captured of the object contains redundant data that allows detailed and accurate 3D reconstruction, the capture and processing time is increased when a vast amount of high resolution images are employed. Moreover, some parts of the object are often missing due to the lack of coverage of all areas. These problems demand a logical selection of the most suitable stereo camera views from the large image dataset. This paper presents a method for clustering and choosing optimal stereo or optionally single images from a large image dataset. The approach focusses on the two key steps of image clustering and iterative image selection. The method is developed within a software application called Imaging Network Designer (IND) and tested by the 3D recording of a gearbox and three metric reference objects. A comparison is made between IND and CMVS, which is a free package for selecting vantage images. The final 3D models obtained from the IND and CMVS approaches are compared with datasets generated with an MMDx Nikon Laser scanner. Results demonstrate that IND can provide a better image selection for MVS than CMVS in terms of surface coordinate uncertainty and completeness.

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