ORB-SLAM method based on local adaptive threshold extraction feature points

doi:10.13474/j.cnki.11-2246.2021.0269

Abstract

Abstract: Localization and mapping of intelligent driving vehicles is one of the key technologies of intelligent driving vehicles. Aiming at the problem of feature points extracted as fixed threshold in ORB-SLAM a local adaptive threshold method is proposed to extract feature points.Firstly, the calculation method of local adaptive threshold is described, and the adaptive threshold is set by image contrast.Secondly, Gaussian image pyramid is constructed on the basis of FAST algorithm and gray-scale centroid method is adopted to solve the scale invariant and rotation problem of feature points. The image grid area is divided in each layer of image pyramid and the contrast of each layer of image grid area is calculated to set the local threshold of each grid area.Finally, feature points are extracted in each image grid area and stored in quadtree structure.The test results show that the number of feature points extracted by the proposed algorithm is 61.9% more than that extracted by the original algorithm in cloudy days, and 23.3% more than that extracted by the original algorithm in sunny days.In cloudy and well-lit scenes, the fluctuation of the number of feature points extracted by this algorithm is less than that of the original algorithm.

Key words: ORB-SLAM, local adaptive threshold, feature point extraction, FAST, image pyramid

CLC Number:

P237

LI Guojun, XU Yanhai, DUAN Jiewen, HAN Shilei. ORB-SLAM method based on local adaptive threshold extraction feature points[J]. Bulletin of Surveying and Mapping, 2021, 0(9): 32-36,48.

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