A loop closure detection method based on semantic segmentation and convolutional neural network

As artificial intelligence flourishes and many related technologies continue to develop, Visual Simultaneous Localization and Mapping, as the "vision" of robots, can utilize a large amount of environmental information. In addition, semantic segmentation can distinguish the background in th...

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Bibliographic Details
Published in2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) pp. 269 - 272
Main Authors Li, Hongyang, Tian, Chengjun, Wang, Lequan, Lv, Hongfu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2021
Subjects
closed loop detection
Convolutional Neural Network (CNN)
Feature extraction
Hardware
Image segmentation
Neural networks
semantic segmentation
Semantics
Simultaneous localization and mapping
Simultaneous Localization and Mapping (SLAM)
Visualization
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Summary:As artificial intelligence flourishes and many related technologies continue to develop, Visual Simultaneous Localization and Mapping, as the "vision" of robots, can utilize a large amount of environmental information. In addition, semantic segmentation can distinguish the background in the image from the moving objects. In recent years, convolutional neural networks have been successfully applied to image processing, and subsequently entered the field of V-SLAM research. Related researchers have tried to directly use convolutional neural networks to extract image features for loop closure detection, but the effect has not surpassed traditional methods. In order to make full use of image information, this paper proposes a loop closure detection and SLAM method based on semantic segmentation and convolutional neural network.
DOI:10.1109/AIEA53260.2021.00063