Real-Time Ellipse Detection for Robotics Applications
This addresses the problem of reliable ellipse detection for robotics applications, such as autonomous UAV landing, but is incremental as it builds on existing methods with specific improvements for real-time performance.
The paper tackles real-time ellipse detection for robotics by proposing a lightweight algorithm that fits ellipses to contours and rejects poor fits, achieving an F1 score of 0.981 on a dataset with over 1500 frames in tasks like UAV landing on fast-moving vehicles.
We propose a new algorithm for real-time detection and tracking of elliptic patterns suitable for real-world robotics applications. The method fits ellipses to each contour in the image frame and rejects ellipses that do not yield a good fit. The resulting detection and tracking method is lightweight enough to be used on robots' resource-limited onboard computers, can deal with lighting variations and detect the pattern even when the view is partial. The method is tested on an example application of an autonomous UAV landing on a fast-moving vehicle to show its performance indoors, outdoors, and in simulation on a real-world robotics task. The comparison with other well-known ellipse detection methods shows that our proposed algorithm outperforms other methods with the F1 score of 0.981 on a dataset with over 1500 frames. The videos of experiments, the source codes, and the collected dataset are provided with the paper at https://theairlab.org/landing-on-vehicle .