CNN Feature Boosted SeqSLAM for Real-Time Loop Closure Detection

BAI Dongdong; WANG Chaoqun; ZHANG Bo; YI Xiaodong; YANG Xuejun

doi:10.1049/cje.2018.03.010

Volume 27 Issue 3

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Article Navigation > Chinese Journal of Electronics > 2018 > 27(3): 488-499

BAI Dongdong, WANG Chaoqun, ZHANG Bo, et al., “CNN Feature Boosted SeqSLAM for Real-Time Loop Closure Detection,” Chinese Journal of Electronics, vol. 27, no. 3, pp. 488-499, 2018, doi: 10.1049/cje.2018.03.010

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BAI Dongdong, WANG Chaoqun, ZHANG Bo, et al., “CNN Feature Boosted SeqSLAM for Real-Time Loop Closure Detection,” Chinese Journal of Electronics, vol. 27, no. 3, pp. 488-499, 2018, doi: 10.1049/cje.2018.03.010

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CNN Feature Boosted SeqSLAM for Real-Time Loop Closure Detection

doi: 10.1049/cje.2018.03.010

1.
College of Computer, National University of Defense Technology, Changsha 410073, China;
2.
National Institute of Defense Technology Innovation, Beijing 100010, China;
3.
State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha 410073, China

Funds: This work is supported by the National Natural Science Foundation of China (No.615307, No.916484, No.61601486), Research Programs of National University of Defense Technology (No.ZDYYJCYJ140601), and State Key Laboratory of High Performance Computing Project Fund (No.1502-02).

More Information

Corresponding author: ZHANG Bo (corresponding author) received the B.E. degree in information engineering from NUDT in 2010 and the Ph.D. degree in wireless communications from the University of Southampton in 2015. He is currently an assistant professor in National Institute of Defense Technology Innovation. His research interests in wireless communications include the design and analysis of cooperative communications, MIMO systems, and network-robotic systems. (Email:zhangbo10@nudt.edu.cn)
Received Date: 2016-12-12
Rev Recd Date: 2017-12-27
Publish Date: 2018-05-10

Abstract

Abstract

This paper proposes an efficient and robust Loop closure detection (LCD) method based on Convolutional neural network (CNN) feature. The primary method is called SeqCNNSLAM, in which both the outputs of the intermediate layer of a pre-trained CNN and the outputs of traditional sequence-based matching procedure are incorporated, making it possible to handle the viewpoint and condition variance properly. An acceleration algorithm for SeqCNNSLAM is developed to reduce the search range for the current image, resulting in a new LCD method called A-SeqCNNSLAM. To improve the applicability of A-SeqCNNSLAM to new environments, O-SeqCNNSLAM is proposed for online parameters adjustment in A-SeqCNNSLAM. In addition to the above work, we further put forward a promising idea to enhance SeqSLAM by integrating the both CNN features and VLAD's advantages called patch based SeqCNNSLAM (P-SeqCNNSLAM), and provide some preliminary experimental results to reveal its performance.
- Simultaneous localization and mapping (SLAM),
- Loop closure detection (LCD),
- Convolutional neural network (CNN),
- SeqSLAM,
- SeqCNNSLAM,
- ASeqCNNSLAM,
- O-SeqCNNSLAM,
- P-SeqCNNSLAM

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References(22)

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