Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (10): 228-232.doi: 10.11896/jsjkx.190900034

• Artificial Intelligence • Previous Articles     Next Articles

Loop Closure Detection Method Based on Unsupervised Deep Learning

WANG Dan1, SHI Chao-xia1, WANG Yan-qing2   

  1. 1 School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
    2 School of Information Engineering,Nanjing Xiaozhuang University,Nanjing 210038,China
  • Received:2019-09-04 Revised:2020-01-09 Online:2020-10-15 Published:2020-10-16
  • About author:WANG Dan,born in 1995,postgra-duate.Her main research interests include visual SLAM and so on.
    SHI Chao-xia,born in 1972,Ph.D,professor,postgraduate supervisor.His main research interests include unmanned vehicle autonomous navigation,SLAM and multi-robot collaboration.
  • Supported by:
    National Natural Science Foundation of China (61371040)

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Abstract: Loop closure detection is one of the most critical parts for simultaneous localization and mapping (SLAM) systems.It can reduce the accumulativeerror in SLAM system.If the tracking is lost during localization and mapping,it can also use the loop closure detection for relocation.Image features learned from neural networks have better environmental invariance and semantic recognition capabilities compared to traditional hand-crafted features.Considering that the landmark-based convolution features can overcome the defect that the whole image features are sensitive to viewpoint changes,this paper proposes a new loop closure detection algorithm.Firstly,it directly identifies the saliency region of the image through the convolutional layer of the convolutional neural network to generate a landmark.And then,it extracts the ConvNet features from the landmarks to generate the final image representations.In order to verify the effectiveness of the algorithm,some comparative experiments were performed on some typical datasets.The rusults show that the proposed algorithm has superior performance,and has highly robust even in drastic viewpoints and appearance changes.

Key words: ConvNet feature, Deep learning, Hand-crafted feature, Loop closure detection, SLAM

CLC Number: 

  • TP391
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