A Feature Extraction Algorithm Based on Blockchain Storage that Combines ORB Feature Points and Quadtree Division

Abstract

In the process of 5G power grid inspection robot moving for a long time, the sensor constantly collects the feature information of the substation. Due to the limited memory capacity, this feature information must be stored on the network, which requires the storage network must have strong security. The storage network based on blockchain can better solve the problem of feature data encryption, and a good feature extraction method can also relieve the pressure of network storage. As the input information of the whole SLAM, feature points play a crucial role in the detection performance and accuracy of the whole SLAM. When the extracted feature points are few or evenly distributed, they cannot express the information of the whole environment, which will make the mapping and localization error of SLAM system larger, and seriously lead to the loss of tracking. In this paper, we first analyze the standard ORB algorithm and the Qtree_ORB algorithm. Aiming at the problems existing in the two algorithms, an improved ORB feature extraction algorithm is developed. For the problem that the feature points extracted by the Qtree_ORB algorithm are too uniform, the maximum division depth of the quadtree is limited according to the number of feature points required for each layer of the image pyramid, which improves the problem that the feature points are too uniform. Finally, we evaluate the performance of the improved algorithm, and analyze the uniformity of feature points to verify the performance and robustness of the improved algorithm.