Evaluation of Feature Detection and Extraction Methods for Landmark Separability in Visual Localization

In this study, the utilization of feature detector methods in landmark-based visual localization research has been evaluated. In contrast to comparative studies of existing feature detection methods, their performance has been assessed in detecting and discerning similarities among environments at distinct locations that pose challenges for perception. Additionally, variables such as varying lighting conditions, camera exposure time, and sensor light sensitivity (ISO value), as well as focus range, have been incorporated into the evaluation. BRISK, Fast, Harris, MinEigen, MSER, ORB, SIFT, SURF feature detector, and descriptor methods have been considered in this assessment. BRISK and MinEigen, notably, have demonstrated superior performance in detecting objects that could serve as landmarks in extremely unambiguous environments compared to other methods. Furthermore, it has been observed that in situations with excessive measurements in camera exposure time, more features can be detected. Additionally, an increase in image sensor light sensitivity has led to a reduction in the number of detected features across all methods. Another noteworthy finding is the dramatic decrease in matched features observed in images captured under varying lighting conditions and the same positions.