Abstract
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.
Highlights
During the last decade, the Simultaneous Localization and Mapping (SLAM) problem has been one of the main research interests in mobile robotics
The map is represented as a sparse point cloud, where each point results from triangulating salient points matched from a pair of stereo images
We evaluate the impact of different state-of-the-art feature extractors on the performance of the Visual SLAM localization method
Summary
The Simultaneous Localization and Mapping (SLAM) problem has been one of the main research interests in mobile robotics. In vision-based SLAM approaches, local image features are used to build a map and simultaneously estimate the robot pose using the environment landmarks represented as the image features. In this way, the map is represented as a sparse point cloud, where each point results from triangulating salient points (image features) matched from a pair of stereo images. A feature extractor is a combination of a salient point (called keypoint) detection procedure and a computation of a unique signature (called descriptor) for each such a detected point. The most commonly used detectors are SIFT [5], SURF [6], STAR [7], GFTT [8], FAST [9], and relatively recently proposed ORB [10], while among the most used descriptors we can mention SIFT, SURF, ORB, BRIEF [11], and BRISK [12]
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