A comparison of feature descriptors for visual SLAM

Feature detection and feature description plays an important part in Visual Simultaneous Localization and Mapping (VSLAM). Visual features are commonly used to efficiently estimate the motion of the camera (visual odometry) and link the current image to previously visited parts of the environment (place recognition, loop closure). Gradient histogram-based feature descriptors, like SIFT and SURF, are frequently used for this task. Recently introduced binary descriptors, as BRIEF or BRISK, claim to offer similar capabilities at lower computational cost. In this paper, we will compare the most popular feature descriptors in a typical graph-based VSLAM algorithm using two publicly available datasets to determine the impact of the choice for feature descriptor in terms of accuracy and speed in a realistic scenario.

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