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SIFT feature detection optimization method based on local area substantial parameter indexes

A local area and parameter index technology, applied in image data processing, instruments, calculations, etc., can solve problems that affect matching accuracy, increase computational complexity, etc., and achieve the effect of improving convergence speed

Inactive Publication Date: 2018-05-11
XINJIANG INST OF ENG
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Problems solved by technology

[0015] However, the image registration algorithm based on locally significant SIFT features uses the improved K-means clustering algorithm for cluster screening, and the number of clusters needs to be determined in advance. A larger number of clusters will not only increase the computational complexity, but also affect Matching accuracy

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  • SIFT feature detection optimization method based on local area substantial parameter indexes
  • SIFT feature detection optimization method based on local area substantial parameter indexes
  • SIFT feature detection optimization method based on local area substantial parameter indexes

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Embodiment Construction

[0040] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below with reference to the accompanying drawings and examples.

[0041] The present invention starts from the statistical characteristics of SIFT feature points and randomly selects a limited number of feature points under the condition of fully ensuring uniform distribution, so as to meet the requirement of EKF convergence on the number of feature points and ensure the accuracy required for scene description.

[0042] The present invention proposes a SIFT map feature point detection and optimization method based on local area salient parameter indicators, and its specific steps are as follows:

[0043] 1) For the current frame image, first detect the corresponding SIFT feature points.

[0044] 2) Divide the image into M×N local areas, and calculate the feature salient parameter index value I of each local area i , ...

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Abstract

The present invention discloses an SIFT (Scale Invariant Feature Transform) feature detection optimization method based on local area substantial parameter indexes. The method comprises the steps of:detecting corresponding SIFT feature points; equally dividing an image into local areas, and selecting local substantial areas; for each local substantial area, selecting features points from the local substantial areas; and performing matching of the selected SIFT feature points with a current feature map. The SIFT feature detection optimization method based on the local area substantial parameter indexes is helpful for the convergence speed of a monocular SLAM (Simultaneous Localization and Mapping) system and is helpful for accurate description of a scene environment.

Description

technical field [0001] The invention relates to the technical field of positioning and map construction, in particular to a SIFT feature detection and optimization method based on local region salient parameter indexes. Background technique [0002] Simultaneous Localization and Mapping (SLAM) of mobile robots in uncertain environments is one of the most challenging key issues in the field of current robot localization and navigation research. The solution to the SLAM problem is the prerequisite for the robot to achieve a high degree of autonomy and intelligence. In recent years, with the development of computer vision technology and the wide application of visual sensors, the research on monocular vision SLAM technology has gradually become an important research direction in the field of SLAM research. The first thing that the vision-based SLAM method must face is the extraction of visual features. The visual SLAM method must obtain stable and sacrificed feature informati...

Claims

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Application Information

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IPC IPC(8): G06T7/33
CPCG06T2207/10004G06T7/33
Inventor 胡衡徐磊付涛刘光辉
Owner XINJIANG INST OF ENG
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