A Novel Approach to Image-Sequence-Based Mobile Robot Place Recognition

Visual place recognition is a challenging problem in simultaneous localization and mapping (SLAM) due to a large variability of the scene appearance. A place is usually described by a single-frame image in conventional place recognition algorithms. However, it is unlikely to completely describe the...

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Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2021-09, Vol.51 (9), p.5377-5391
Main Authors: Yuan, Jing, Zhu, Wenbin, Dong, Xingliang, Sun, Fengchi, Zhang, Xuebo, Sun, Qinxuan, Huang, Yalou
Format: Article
Language:English
Subjects:
Algorithms
Automation
Bag-of-words (BoW)
echo state network (ESN)
Feature extraction
Image acquisition
Image recognition
Image segmentation
image sequence
Image sequences
mobile robot
Mobile robots
Object recognition
place recognition
Robots
Visualization
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Summary:Visual place recognition is a challenging problem in simultaneous localization and mapping (SLAM) due to a large variability of the scene appearance. A place is usually described by a single-frame image in conventional place recognition algorithms. However, it is unlikely to completely describe the place appearance using a single frame image. Moreover, it is more sensitive to the change of environments. In this article, a novel image-sequence-based framework for place detection and recognition is proposed. Rather than a single frame image, a place is represented by an image sequence in this article. Position invariant robust feature (PIRF) descriptors are extracted from images and processed by the incremental bag-of-words (BoWs) for feature extraction. The robot automatically partitions the sequentially acquired images into different image sequences according to the change of the environmental appearance. Then, the echo state network (ESN) is applied to model each image sequence. The resultant states of the ESN are used as features of the corresponding image sequence for place recognition. The proposed method is evaluated on two public datasets. Experimental comparisons with the FAB-MAP 2.0 and SeqSLAM are conducted. Finally, a real-world experiment on place recognition with a mobile robot is performed to further verify the proposed method.
ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2019.2956321