# Computer Vision-Based Optical Odometry Sensors: A Comparative Study of Classical Tracking Methods for Non-Contact Surface Measurement

## TL;DR

This paper compares classical computer vision methods for measuring surface movement without physical contact, aiming to improve precision in metrology and robotics.

## Contribution

The study provides a systematic framework for evaluating and selecting classical tracking algorithms for optical odometry using controlled synthetic data.

## Key findings

- Phase correlation, template matching, and optical flow were systematically compared for 2D displacement measurement.
- Performance evaluations used synthetic image sequences with subpixel-accurate ground truth and controlled test conditions.
- The framework enables informed algorithm selection based on application requirements rather than trial and error.

## Abstract

This article presents a principled framework for selecting and tuning classical computer vision algorithms in the context of optical displacement sensing. By isolating key factors that affect algorithm behavior—such as feed window size and motion step size—the study seeks to move beyond intuition-based practices and provide rigorous, repeatable performance evaluations. Computer vision-based optical odometry sensors offer non-contact, high-precision measurement capabilities essential for modern metrology and robotics applications. This paper presents a systematic comparative analysis of three classical tracking algorithms—phase correlation, template matching, and optical flow—for 2D surface displacement measurement using synthetic image sequences with subpixel-accurate ground truth. A virtual camera system generates controlled test conditions using a multi-circle trajectory pattern, enabling systematic evaluation of tracking performance using 400 × 400 and 200 × 200 pixel feed windows. The systematic characterization enables informed algorithm selection based on specific application requirements rather than empirical trial-and-error approaches.