Impact assessment of image feature extractors on the performance of SLAM systems

TAIHÚ PIRE; THOMAS FISCHER; JAN FAIGL

Písek

Conferencia; PAIR'14 / PAIR'15 Student Conference on Planning in Artificial Intelligence and Robotics; 2015

Czech Technical University in Prague

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 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 requirements among the evaluated combinations of the detectors and descriptors.