SuperFAST: Model-based adaptive corner detection for scalable robotic vision

In this study, we propose a novel solution to regulate the amount of interest points extracted from an image without significant additional computational cost. Our method acts at the very beginning of the detection process by using a corner occurrence model in order to predict the optimal threshold for a user-defined number of detections. Compared to existing approaches which guarantee a reasonable amount of corners by using a low threshold and then pruning the result, our approach is faster and more regular in terms of computation time as it avoids scoring and sorting the detected corners. Using the FAST detector as testbed, the strategy outlined in this article is evaluated in typical environments for robotics applications, and we report improved detection reliability during important scene variations. Taking into account the underlying visual navigation algorithms, we show that by regularizing the data input our solution facilitates a stable processing load, lower inter-frame computation time, and robustness to scene variations.