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Robot positioning and navigation method based on bag-of-words tree cluster model

A robot positioning and tree model technology, applied in navigation, surveying and navigation, navigation computing tools, etc., can solve the problems of large influence on map building, expensive laser sensors, and inability to deal with black light-absorbing substances, etc., and achieve low-cost Effect

Active Publication Date: 2017-06-13
PEKING UNIV SHENZHEN GRADUATE SCHOOL +1
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Problems solved by technology

However, the laser sensor is very expensive, and cannot handle black light-absorbing substances or black environments; it cannot handle the modeling of transparent substances such as glass that will change the laser propagation path; jitter has a great impact on mapping

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Embodiment Construction

[0028] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described below through specific embodiments and accompanying drawings.

[0029] 1. Establishment of bag-of-words tree group model based on DBoW2

[0030] DBoW2 (References: Galvez-López D, Tardos J D. Bags of Binary Words for Fast Place Recognition in Image Sequences[J].IEEE Transactions on Robotics,2012,28(5):1188-1197.) Structure the image describe. The idea of ​​DBoW2 is to integrate image features into visual words, transform the image feature space into a discrete visual dictionary, and store visual words in a tree structure to speed up the retrieval of features and similar images. Map the new image features to the nearest neighbor visual dictionary in the visual dictionary, and then calculate the similarity of the image by calculating the distance between the visual dictionaries, so as to complete tasks such ...

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Abstract

The invention discloses a robot positioning and navigation method based on a bag-of-words tree cluster model. The method comprises the following steps: 1) inputting a long-time video stream containing a low dynamic indoor scene change; 2) performing off-line feature extraction on an image of the video stream; 3) clustering the obtained features through a k-means++ algorithm; 4) performing iterative clustering on the subspace obtained after clustering; 5) building a bag-of-words tree model for features after iterative clustering; 6) building a bag-of-words tree cluster model; 7) summarizing the motion law of a low dynamic object through a statistical principle; 8) analyzing and determining a bag-of-words tree model corresponding to the current moment based on the motion law according to the bag-of-words tree cluster model; and 9) using the determined bag-of-words tree model for loopback detection to realize robot positioning and navigation in a low dynamic environment. According to the invention, long-time self positioning and navigation of a robot can be realized in a low dynamic environment such as indoor space and the like at low cost under the condition of avoiding the use of expensive laser sensors.

Description

technical field [0001] The invention belongs to the technical field of robot autonomous positioning and navigation and robot vision, and in particular relates to a robot positioning and navigation method based on a bag-of-words model; by constructing a bag-of-words tree group model based on time, the words are evaluated based on a probability estimation model in a low dynamic environment. The bag-of-words model tree is selected, and the bag-of-words tree group model can provide dependencies for long-term indoor synchronous positioning and loop closure detection of mapping robots. Background technique [0002] Robot autonomous positioning and navigation is an interdisciplinary subject. In the past ten years, robot autonomous positioning and navigation and robot vision have made great progress. Although robot vision has achieved high precision in specific scenes and databases, and some service robots have come into life, the application of robot vision in autonomous navigation...

Claims

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Application Information

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IPC IPC(8): G01C21/20
CPCG01C21/206
Inventor 刘宏金永庆宋章军张国栋赵晨阳吴观明
Owner PEKING UNIV SHENZHEN GRADUATE SCHOOL
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