3D perception from binocular vision for a low cost humanoid robot NAO

Nefti-Meziani, Samia; Manzoor, Umar; Davis, Steve; Pupala, Suresh Kumar

Abstract

Depth estimation is a classical problem in computer vision and after decades of research many methods have been developed for 3D perception like magnetic tracking, mechanical tracking, acoustic tracking, inertial tracking, optical tracking using markers and beacons. The vision system allows the 3D perception of the scene and the process involves: (1) camera calibration, (2) image correction, (3) feature extraction and stereo correspondence, (4) disparity estimation and reconstruction, and finally, (5) surface triangulation and texture mapping. The work presented in this paper is the implementation of a stereo vision system integrated in humanoid robot. The low cost of the vision system is one of the aims to avoid expensive investment in hardware when used in robotics for 3D perception. In our proposed solution, cameras are highly utilized as in our opinion they are easy to handle, cheap and very compatible when compared to the hardware used in other techniques. The software for the automated recognition of features and detection of the correspondence points has been programmed using the image processing library OpenCV (Open Source Computer Vision) and OpenGL (Open Graphic Library) is used to display the 3D models obtained from the reconstruction. Experimental results of the reconstruction and models of different scenes are shown. The results obtained from the program are evaluated comparing the size of the objects reconstructed with that calculated by the program.

Citation

Nefti-Meziani, S., Manzoor, U., Davis, S., & Pupala, S. K. (2015). 3D perception from binocular vision for a low cost humanoid robot NAO. Robotics and Autonomous Systems, 68, 129-139. https://doi.org/10.1016/j.robot.2014.12.016

• Journal Article Type: Article
• Acceptance Date: Dec 5, 2014
• Online Publication Date: Feb 3, 2015
• Publication Date: 2015-06
• Deposit Date: Jun 8, 2022
• Journal: Robotics and Autonomous Systems
• Print ISSN: 0921-8890
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 68
• Pages: 129-139
• DOI: https://doi.org/10.1016/j.robot.2014.12.016
• Public URL: https://hull-repository.worktribe.com/output/1768369