A loop detection method based on word bag model

A technology of bag-of-words model and detection method, which is applied in character and pattern recognition, instruments, computer components, etc., can solve the problems that the system cannot perform loopback detection and loopback detection, and achieve the effect of accurate and effective detection of loopback and high recall rate

Active Publication Date: 2019-03-01
SUN YAT SEN UNIV
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Problems solved by technology

[0005] In order to overcome the fact that the bag-of-words model uses visual features composed of FAST key points and BRIEF descriptors, the system cannot effectively perform loop closure detection in scenes containing plane rotation and scale scaling, and avoid errors caused by abnormal normalization factors Normalization leads to the problem that the system cannot effectively perform loopback detection when the subject moves too fast or too slow and turns. The present invention proposes a loopback detection method based on the bag of words model. The technical solution adopted in the present invention is:

Method used

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  • A loop detection method based on word bag model
  • A loop detection method based on word bag model
  • A loop detection method based on word bag model

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Embodiment 1

[0037] figure 1 As shown, a loop detection method based on the bag-of-words model includes the following steps:

[0038] S10. Vector transformation of bag-of-words model: extract ORB visual features from the images acquired by the system, and convert the images into numerical vectors according to the distribution of ORB visual features in the visual dictionary of the bag-of-words model; provide a fast and effective comparison between images in the future in accordance with;

[0039] S20. Calculation of the similarity score between images: calculate the corresponding similarity score based on the current image and the previously acquired numerical vector of each image, for any two numerical vectors v 1 and v 2 , using the similarity assessed by the L1 norm:

[0040]

[0041]The value of this similarity score is distributed between 0 and 1. When the two images have no similarity at all, the corresponding similarity score is 0, and when the two images are completely consist...

Embodiment 2

[0054] This embodiment provides a loop detection method based on the bag-of-words model, and applies the loop-closing detection method based on the bag-of-words model corresponding to the present invention to a visual SLAM system based on key frame technology with an RGB-D camera as a sensor, and disclosed in Dataset TUM dataset selects multiple image sequences to evaluate the performance of the algorithm.

[0055] Extract 1000 ORB visual features from each image and transform these visual features into bag-of-words model vectors to represent the image according to their distribution in the visual dictionary. Then the numerical vector is compared with the numerical vector corresponding to the previously acquired image to obtain a normalized similarity score between images. The confidence parameter α is set to 0.8 to get the initial loop-closing candidates and group adjacent loop-closing candidates together as a class. Calculate an overall similarity score for each class of lo...

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Abstract

The invention relates to a loop detection method based on a word bag model. The lexical bag model adopts binary visual features ORB with rotation invariance and scale perception. This visual feature has the same performance as SIFT feature and SURF feature and the same computational efficiency as the visual feature composed of FAST key points and BRIEF descriptor. It is a visual feature with low computational complexity and high feature salience. The present invention employs a bag-of-words model that relies on the visual features, so that loop detection can be efficiently performed in a scenewith plane rotation and scale scaling. At the same time, the normalization method of similarity score is improved by calculating and maintaining the mean value of a normalization factor and replacingthe normalization factor when the normalization factor is abnormal. This normalization method enables the system to effectively detect loops in the event that the main body moves too fast or too slowly and steers.

Description

technical field [0001] The present invention relates to the field of visual SLAM, and more specifically, to a loop detection method based on a bag-of-words model. Background technique [0002] Loop closure detection based on the bag-of-words model is currently the mainstream practice in visual SLAM. The bag-of-words model can convert images into numerical vectors according to the distribution of visual features extracted from images in the visual dictionary, thereby realizing fast and effective comparison between images. [0003] The performance of the bag-of-words model depends on the visual features it uses. The early adoption of SIFT features and SURF features consumes a lot of time in feature extraction and matching, which aggravates the system burden. In "D.Galvez-Lopez, and J.D.Tardos, "Real-time loop detection with bags of binary words," IEEE / RSJInternational Conference on Intelligent Robots and Systems, pp.51–58, 2011" adopted by FAST The visual features composed of...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/22G06F18/24
Inventor 田浩辰吴贺俊
Owner SUN YAT SEN UNIV
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