Abstract
ORB (Oriented FAST and Rotated BRIEF) feature is wildly applied in visual SLAM because of its excellent computational efficiency and stability. Aiming at the problem of uneven distribution of ORB feature, and improving the calculate efficiency of feature extraction at the same time, we proposed an ORB feature extraction algorithm based on improved quadtree in this paper. The proposed algorithm will select the threshold adaptively for FAST extraction according to the gray image instead of the value set artificially. And then we set different depth of quadtree according to the expected feature number which decreases as the number of image pyramid layers increases to reduce redundancy. The remained key points selected by Harris score will distribute well in the image. The results show that the proposed algorithm can improve the uniformity of ORB feature, and reduce feature extraction time compared to the algorithm in ORB_SLAM, it has certain application value for the realization of real-time SLAM system.
Highlights
SLAM (Simultaneous Localization and Mapping) is the process through sensor to reconstruct the environment and estimate the position of robots at the same time [1], [2]
The algorithm solved the problem of time consumption, and it can operate in real-time without GPU. This algorithm improves the extraction efficiency of feature extraction, ORB feature points are still unevenly distributed in the image plane and easy to aggregation, which reduces the accuracy of subsequent feature matching and pose estimation
Yao et al.: Adaptive Uniform Distribution ORB Based on Improved Quadtree eliminate the redundant feature points, but the algorithm still adopts the traditional quadtree structure, so the computational efficiency needs to be improved
Summary
SLAM (Simultaneous Localization and Mapping) is the process through sensor to reconstruct the environment and estimate the position of robots at the same time [1], [2]. The algorithm solved the problem of time consumption, and it can operate in real-time without GPU This algorithm improves the extraction efficiency of feature extraction, ORB feature points are still unevenly distributed in the image plane and easy to aggregation, which reduces the accuracy of subsequent feature matching and pose estimation. J. Yao et al.: Adaptive Uniform Distribution ORB Based on Improved Quadtree eliminate the redundant feature points, but the algorithm still adopts the traditional quadtree structure, so the computational efficiency needs to be improved. The overall image contrast is taken into account when extracting feature points, and the maximum depth of quadtree is set according to different pyramid layers, so as to eliminate redundant feature points and improve the efficiency of feature detection.
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