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Mobile robot vision SLAM loopback detection method based on K-SVD dictionary learning

A mobile robot and detection method technology, applied in the direction of instruments, computer components, character and pattern recognition, etc., can solve the problems of bag-of-words model data pressure, large amount of calculation, loopback detection speed cannot be improved, etc., to achieve position drift problem Fast, speed-enhancing effects

Inactive Publication Date: 2020-12-08
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0004] All of the above methods can detect loops, but there are still problems that the data pressure of the bag-of-words model is high, the amount of calculation is large when comparing similarities, and the speed of loop detection cannot be improved.

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  • Mobile robot vision SLAM loopback detection method based on K-SVD dictionary learning
  • Mobile robot vision SLAM loopback detection method based on K-SVD dictionary learning
  • Mobile robot vision SLAM loopback detection method based on K-SVD dictionary learning

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[0040] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic concept of the present invention, and the following embodiments and the features in the embodiments can be combined with each other in the case of no conflict.

[0041] see figure 1 , figure 1 Shown is the mobile robot visual SLAM loop closure detection method based on K-SVD dictionary learning according to the prese...

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Abstract

The invention relates to a mobile robot vision SLAM loopback detection method based on K-SVD dictionary learning, and belongs to the technical field of robots. The method specifically comprises the following steps: S1, performing a K-SVD self-learning algorithm on a data set to obtain a K-SVD dictionary; S2, for an image detected again, based on a K-SVD dictionary, obtaining sparse representationof a measurement signal through a matching pursuit algorithm; and S3, calculating by utilizing an image representation method based on complexity, and comparing with a set threshold value to detect loopback. According to the invention, a stored information amount and a calculated amount are greatly reduced so that a loopback detection process is accelerated, and a position drift problem is solvedmore quickly.

Description

technical field [0001] The invention belongs to the technical field of robots, relates to the field of positioning of mobile robots, and in particular to a visual SLAM loopback detection method for mobile robots based on K-SVD dictionary learning. Background technique [0002] In the simultaneous localization and mapping (SLAM) problem, it is necessary to detect previously visited locations, thereby reducing estimation errors, and to detect loopbacks by comparing sensor observations through similarity metrics. Bag of words is often used The model (Bag-of-Words, BoW) regards features as words and compares the consistency of words in two pictures to determine whether the two pictures belong to the same scene. In order to be able to classify features into words, training is required. For a dictionary, the document length of the bag-of-words model is equal to the number of known words, so when using the bag-of-words model, it is necessary to reduce the vocabulary pressure. [0...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46G06K9/62
CPCG06V10/513G06V10/44G06V10/751G06F18/2136
Inventor 李帅永杨雪梅
Owner CHONGQING UNIV OF POSTS & TELECOMM
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