Improved feature point extraction and mismatch eliminating algorithm

Abstract

For the existing image matching algorithms, some inherent shortcomings, such as high mismatch rate and low computational efficiency, give rise to a bad influence on the performance of Visual Simultaneous Localization and Mapping (VSLAM). In this paper, a Grid-Based Motion Statistics for Fast and Random Sample Consensus (GMS-RANSAC) method combining with Multi-Probe Location Sensitive Hash (LSH)-based Adaptive and Generic Corner Detection Based on the Accelerated Segment Test and Oriented FAST and Rotated BRIEF (AGAST-ORB) algorithm is proposed to improve the real time and accuracy of image matching. To this end, the AGAST algorithm and the multi-probe LSH algorithm are firstly integrated into the traditional ORB algorithm to obtain the initial matching set. Specifically, the image feature points are extracted by the AGAST algorithm and then the main direction of feature points is given according to the intensity centroid method to guarantee the rotary invariant of feature points. Based on the extracted feature points, the multi-probe LSH algorithm, benefiting from its high time efficiency, is used to generate the initial matching pairs. In what follows, a GMS-RANSAC algorithm, which is improved by adding a directional similarity constraint model and the traditional RANSAC algorithm, is performed to improve the accuracy of eliminating result further. Finally, the performance test is implemented via a Mikolajczyk standard data set and it is verified that the proposed algorithm has higher matching precision and matching efficiency than traditional image matching algorithms.

STDC-SLAM: A Real-Time Semantic SLAM Detect Object by Short-Term Dense Concatenate Network

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