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Computer Vision-Based Optical Odometry Sensors: A Comparative Study of Classical Tracking Methods for Non-Contact
Ignas Andrijauskas1, Marius Šumanas1, Andrius Dzedzickis1
1Department of Mechatronics, Robotics and Digital Manufacturing, Faculty of Mechanics, Vilnius Gediminas Technical University (VILNIUS TECH), 10223 Vilnius, Lithuania.
Sensors (Basel, Switzerland)
|October 16, 2025
Summary
This study provides a framework for choosing computer vision algorithms for optical displacement sensing. It systematically evaluates phase correlation, template matching, and optical flow for precise 2D surface measurements.
Area of Science:
- Computer Vision
- Metrology
- Robotics
Background:
- Optical displacement sensing utilizes computer vision for non-contact, high-precision measurements.
- Classical algorithms are often selected based on intuition rather than rigorous evaluation.
Purpose of the Study:
- To develop a principled framework for selecting and tuning computer vision algorithms for optical displacement sensing.
- To provide rigorous, repeatable performance evaluations of classical tracking algorithms.
Main Methods:
- Systematic comparative analysis of phase correlation, template matching, and optical flow algorithms.
- Utilized synthetic image sequences with subpixel-accurate ground truth.
- Employed a virtual camera system with a multi-circle trajectory pattern for controlled testing.
Main Results:
- Evaluated algorithm performance across different feed window sizes (400x400, 200x200 pixels) and motion step sizes.
- Characterized tracking performance for 2D surface displacement measurement.
- Identified key factors influencing algorithm behavior.
Conclusions:
- The systematic characterization enables informed algorithm selection for optical displacement sensing.
- Moves beyond empirical trial-and-error for more reliable performance.
- Facilitates optimal algorithm choice based on specific application requirements.
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