This paper presents the performances of a cornerdetector and a region detector in vision-based Simultaneous Localization and Mapping (SLAM). In vision-based SLAM, a feature detection process fordata-association is t...
详细信息
This paper presents the performances of a cornerdetector and a region detector in vision-based Simultaneous Localization and Mapping (SLAM). In vision-based SLAM, a feature detection process fordata-association is t...
详细信息
ISBN:
(纸本)9781479905492
This paper presents the performances of a cornerdetector and a region detector in vision-based Simultaneous Localization and Mapping (SLAM). In vision-based SLAM, a feature detection process fordata-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 cornerdetector. There are conflicting research results for the repeatability of cornerdetectors andregion detectors. This paper shows SLAM results using a cornerdetector 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 comerdetector is more efficient than the region detector for the work performed in this paper.
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