Evaluation of Image Detection and Description Algorithms for Application in Monocular SLAM

Related Work

At present, there is a wide range of algorithms for the detection and description of image features, therefore a comparative study of those algorithms becomes necessary to evaluate their behavior in a SLAM system. Although the comparative work of these technologies focuses on artificial vision applications, three important studies in the field of visual localization and SLAM should be noted. Firstly, the work of Gauglitz et al. (2011) presents an extensive analysis of the various combinations between detection and description algorithms for a visual localization application. Secondly, Gil et al. (2010) conduct a comparative study on detectors and descriptors for visual SLAM, which is based on the measurement of the repetition in which features appear in successive images (recall) and on the precision of the features obtained by the compared algorithms. However, despite the comparison being useful to improve the system’s precision, this study does not consider the uncertainty inherent to SLAM. Finally, Klippenstein and Zhang’s (2009) work proposes a new methodology that employs various detectors in order to evaluate the performance of a SLAM system by means of a test of accumulated consistency and uncertainty.