Feature Matching Comparison with Limited Computing Power Device for Autonomous Driving

This research-to-practice full paper describes a study on the performance of feature-matching algorithms in constrained computational environments, focusing on autonomous driving using low-end hardware like the Raspberry Pi 4B. We evaluate algorithms such as ORB, AKAZE, BRISK, and SIFT, examining their efficiency, accuracy, and robustness under various conditions. While ORB offers speed, AKAZE and BRISK demonstrate more consistent performance. To mitigate the gap between theoretical analysis and practical application, we integrate these findings into a robotics course through a project-based learning (PBL) approach. The comparison analysis provides the instructor with the necessary insights to guide students, as the research setting closely mirrors the course project. This hands-on project not only deepens students' understanding of computer vision but also hones critical problem-solving skills essential for modern engineering challenges. Future work will extend this study to other single-board computers and explore advanced computational techniques like parallel computing and GPU acceleration.