Closed-loop detection method for indoor scene recognition

A closed-loop detection and indoor scene technology, which is applied in the closed-loop detection field of indoor scene recognition, can solve problems such as relatively few indoor environment studies, and achieve the effect of solving scene confusion, high computing efficiency, and strong application value

Active Publication Date: 2017-06-27
SHANGHAI JIAO TONG UNIV
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AI Technical Summary

Problems solved by technology

[0003] Most of the current closed-loop detection algorithms are optimized for

Method used

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  • Closed-loop detection method for indoor scene recognition
  • Closed-loop detection method for indoor scene recognition
  • Closed-loop detection method for indoor scene recognition

Examples

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Embodiment Construction

[0058] The present invention will be further described in detail below through specific embodiments in conjunction with the accompanying drawings.

[0059] To solve the problem of inaccurate indoor scene recognition, this example provides a closed-loop detection method for indoor scene recognition. The flow chart is as follows figure 1 As shown, it specifically includes the following steps.

[0060] S1: Collect the current scene image, use the FAST algorithm to extract the feature points of the current scene image, and use the LSD algorithm to extract the feature lines of the current scene image.

[0061] When the robot moves indoors, the robot uses its own camera to collect the current scene image, and extracts the feature points and feature lines of the current scene image. Specifically, the FAST (Features from acce leratedsegment test corner detection method) algorithm is used to extract the current scene image The feature points and feature lines of the current scene imag...

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Abstract

The invention relates to a closed-loop detection method for indoor scene recognition. The closed-loop detection method comprises the following steps that: a current scene image is collected, the FAST algorithm is utilized to extract the feature points of the current scene image, and the LSD algorithm is utilized to extract the feature lines of the current scene image; the ORB is adopted to generate the descriptor vectors of the feature points, and the BRLD is adopted to generate the descriptor vectors of the feature lines; the K-means clustering algorithm is adopted to cluster all the descriptor vectors to generate a visual vocabulary, and the BOW vector of the current scene image is generated by using the visual vocabulary; and the similarity of the BOW vector of the current scene image and the BOW vector of a stored historical scene image is calculated, the consistency of the current scene image is detected, so that whether a closed loop occurs on the current scene image is judged. According to the closed-loop detection method of the invention, the feature lines are additionally adopted in closed-loop detection, sites which have been visited are identified in an indoor environment where feature points are scarce; and since most of feature lines in the indoor environment scene are static, and after the feature lines are introduced, influence on the closed-loop detection algorithm by the change of dynamic objects in the scene is small, and the problem of scene confusion can be better solved.

Description

technical field [0001] The invention relates to the technical field of mobile robot navigation, in particular to a closed-loop detection method for indoor scene recognition. Background technique [0002] Closed-loop detection is a very important module in the SLAM system. The purpose is to allow the robot to identify the places it has visited, correct the cumulative error of SLAM, and ensure the consistency of the map. Loop closure detection is actually an image recognition problem. The mainstream algorithm is generally based on the BoW (Bag of Words) framework, using different types of feature points as a solution. [0003] Most of the current closed-loop detection algorithms are optimized for outdoor scenes, and there are relatively few studies on indoor environments. Indoor scenes are quite different from outdoor scenes in both the number of features and the type of features. It is very obvious that outdoor scenes generally have rich feature points, while the number of f...

Claims

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V20/36
Inventor 庄诗伟邹丹平裴凌刘佩林郁文贤徐昌庆
Owner SHANGHAI JIAO TONG UNIV
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