Degree
Master of Science
Program
Computer Science
Supervisor
Steven Beauchemin
2nd Supervisor
Michael Bauer
Co-Supervisor
Abstract
The availability of affordable depth sensors in conjunction with common RGB cameras, such as the Microsoft Kinect, can provide robots with a complete and instantaneous representation of the current surrounding environment. However, in the problem of calibrating multiple camera systems, traditional methods bear some drawbacks, such as requiring human intervention. In this thesis, we propose an automatic and reliable calibration framework that can easily estimate the extrinsic parameters of a Kinect sensor network. Our framework includes feature extraction, Random Sample Consensus and camera pose estimation from high accuracy correspondences. We also implement a robustness analysis of position estimation algorithms. The result shows that our system could provide precise data under certain amount noise.
Keywords
Kinect, Multiple Camera Calibration, Feature Points Extraction, Correspondence, RANSAC
Recommended Citation
Li, Xiaoyang, "Feature Based Calibration of a Network of Kinect Sensors" (2018). Electronic Thesis and Dissertation Repository. 5180.
https://ir.lib.uwo.ca/etd/5180