Self-Calibration for Embedded Stereo Vision System

Stereo Vision allows accurately measuring scenes in 3D – provided that the cameras are well calibrated (camera pose, intrinsic parameters). Especially in harsh industrial or automotive contexts, the mechanical vibrations, temperature variations, and material fatigue affect opto-mechanical properties of the stereo-rig and its measurement accuracy.A jointly developed intelligent door opener (embedded stereo-system: reglomat.bircher.com) can detect, discriminate (humans, vehicles), and track 3D objects in real-time. This enables adequate door openings, e.g. suppressing cross-traffic and accounting for the object’s height to minimize energy loss in cooling storage applications. Because calibration drifts can cause e.g. inappropriate openings/closing, periodic or continuous recalibration of the stereo-rig is advantageous.The goal of this thesis is to explore, test and analyze different methods and algorithms for continuous stereo-vision calibration. The solution has to determine the extrinsic and intrinsic camera parameters without any markers or reference objects. Its performance will be measured mainly in overall accuracy, but also calculation time and memory consumption.

Tasks

  • Explore current methods, frameworks and approaches for marker-less self-calibration of stereo camera systems
  • Implement promising approaches on both a standard computer and the target device
  • Evaluate and compare the results of different calibration methods

References

Kind of Work
40% Theory, 40% Implementation, 20% Benchmarking

Requirements

  • Experience with OpenCV or similar tools advantageous
  • Strong interest in Stereo Vision and Computer Vision
  • Solid mathematical understanding is helpful

Time & Effort
Master’s Thesis, 1 Person

Contact
fabian.schenkel@scs.ch

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