Visual SLAM is widely known in robotics for computing, concurrently, the odometry of a robot and construct a 3D navigation map with only a camera. In visual SLAM systems, detection and description of local features are extremely important because they identify unique and invariant points in an observed frame. Although there are various detectors and descriptors, the proper detector/descriptor combination for extraction has not yet been generalized for the problem. In this work, a comprehensive performance evaluation of combinations for different feature detectors and descriptors is presented. This evaluation will help determine the best detector/descriptor combination for designing a visual SLAM system based on RGB-D data. The considered methods are evaluated in terms of accuracy and robustness in both, a single and overall visual SLAM system.
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