Abstract
Front-end feature tracking based on vision is the process in which a robot captures images of its surrounding environment using a camera while in motion. Each frame of the image is then analyzed to extract feature points, which are subsequently matched between pairwise frames to estimate the robot’s pose changes by solving for the variations in these points. While feature matching methods that rely on descriptor-based approaches perform well in cases of significant lighting and texture variations, the addition of descriptors increases computational cost and introduces instability. Therefore, in this paper, a novel approach is proposed that combines sparse optical flow tracking with Shi-Tomasi corner detection, replacing the use of descriptors. This new method offers improved stability in situations of challenging lighting and texture variations while maintaining lower computational cost. Experimental results, validated using the OpenCV library on the Ubuntu operating system, demonstrate the algorithm's effectiveness and efficiency.
Data Availability Statement
Data will be made available on request.
Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 62173007, Grant 62006008, and Grant 62203020; in part by the Project of Humanities and Social Sciences (Ministry of Education in China, MOC) under Grant 22YJCZH006.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
Cite This Article
APA Style
Li, J., Wang, B., Ma, H., Gao, L., & Fu, H. (2024). Visual Feature Extraction and Tracking Method Based on Corner Flow Detection. ICCK Transactions on Intelligent Systematics, 1(1), 3–9. https://doi.org/10.62762/TIS.2024.136895
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