Comparison of corner detector and region detector for vision-based SLAM

This paper presents the performances of a corner detector and a region detector in vision-based Simultaneous Localization and Mapping (SLAM). In vision-based SLAM, a feature detection process for data-association is the first step. Therefore, the repeatability of a feature detector is important. Feature detectors are generally categorized as the region detector and the corner detector. There are conflicting research results for the repeatability of corner detectors and region detectors. This paper shows SLAM results using a corner detector and a region detector. Extended Kalman Filters (EKF) are used for implementing the two SLAM methods. The localization errors are evaluated quantitatively. From the experimental results, it is demonstrated that the corner detector is more efficient than the region detector for the work performed in this paper.