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Visual SLAM (Simultaneous Localization And Mapping) loop detection method based on distance metric learning

A distance measurement and detection method technology, applied in biological neural network models, navigation computing tools, neural architectures, etc., can solve the problems of a large amount of computation and high image feature dimensions, and achieve the effect of reducing the amount of computation

Active Publication Date: 2019-10-01
SUZHOU RUIJIU INTELLIGENT TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the feature dimension of the picture extracted by the CNN model is very high, so the similarity measurement requires a large amount of calculation, which is a big challenge to real-time performance.

Method used

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  • Visual SLAM (Simultaneous Localization And Mapping) loop detection method based on distance metric learning
  • Visual SLAM (Simultaneous Localization And Mapping) loop detection method based on distance metric learning
  • Visual SLAM (Simultaneous Localization And Mapping) loop detection method based on distance metric learning

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Embodiment Construction

[0049]Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0050] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

[0051] The system flowchart of the method of the present invention is as figure 1 As shown, a visual SLAM loop detection method based on distance metric learning disclosed by the present invention, its specific steps are as follows:

[0052] Step 1. On the basis of the pre-trained CNN model, use the training set to optimize, and then extract image features. The type of training set and the structure of CNN are ...

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Abstract

The invention discloses a visual SLAM (Simultaneous Localization And Mapping) loop detection method based on distance metric learning. The method comprises the following steps: pre-training CNN (Convolutional Neural Network) models and optimizing by using a training set; equally dividing pictures of the training set into k groups, simultaneously inputting k CNN pre-trained models, wherein the k CNN pre-trained models share parameters; constructing tuples by using a tuple construction method, and training; optimizing the CNN models according to a distance relation between the tuples after constructing the tuples of all the scenes; directly ending training and entering a testing stage when no appropriate tuple is constructed in the step four; and carrying out actual robot loop detection application by using the optimized CNN models. Through the visual SLAM loop detection method, the technical problem that appearance change and visual angle change simultaneously have robustness is solved;the computation amount of similarity measurement is reduced.

Description

technical field [0001] The invention discloses a visual SLAM loop detection method based on distance metric learning, and relates to the technical field of robot mobile positioning. Background technique [0002] Visual SLAM (Simultaneous Localization and Mapping) is a key technology in the field of mobile robotics. In a SLAM system, a robot will build a model of its surroundings and simultaneously estimate its trajectory. A typical visual SLAM system usually consists of the following modules: visual odometry, back-end optimization, loop detection and mapping. Among them, the loop detection module is used to automatically detect whether the robot has ever been to a certain place. If the loop closure is successfully detected, the robot will be able to provide additional constraints for the back-end optimization and reduce the optimization error. [0003] During actual navigation, the environment around the robot may change. These changes can be divided into changes in appe...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/20G06N3/04
CPCG01C21/20G06N3/045
Inventor 高瑜陈良
Owner SUZHOU RUIJIU INTELLIGENT TECH CO LTD