IMPACT ASSESSMENT OF IMAGE FEATURE EXTRACTORS ON THE PERFORMANCE OF SLAM SYSTEMS

IMPACT ASSESSMENT OF IMAGE FEATURE EXTRACTORS ON THE PERFORMANCE OF SLAM SYSTEMS

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This work evaluates an impact of image feature extractors on the performance of a visual SLAM method in terms of pose accuracy and computational requirements.In particular, the S-PTAM (Stereo Parallel Tracking and Mapping) method is considered as the visual anodized pearl price xbox SLAM framework for which both the feature detector and feature descriptor are parametrized.The evaluation was performed with a standard dataset with ground-truth information and six feature detectors and four descriptors.The presented results indicate that the combination of the GFTT detector and the BRIEF descriptor provides the best trade-off between the localization precision and computational merlin wizard costume requirements among the evaluated combinations of the detectors and descriptors.