DeepSLAM: A Robust Monocular SLAM System With Unsupervised Deep Learning

In this article, we propose DeepSLAM, a novel unsupervised deep learning based visual simultaneous localization and mapping (SLAM) system. The DeepSLAM training is fully unsupervised since it only requires stereo imagery instead of annotating ground-truth poses. Its testing takes a monocular image s...

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Veröffentlicht in:IEEE transactions on industrial electronics (1982) Jg. 68; H. 4; S. 3577 - 3587
Hauptverfasser: Li, Ruihao, Wang, Sen, Gu, Dongbing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Artificial neural networks
Coders
Computer architecture
Deep learning
Depth estimation
Imagery
machine learning
Optimization
Outliers (statistics)
Performance evaluation
Pose estimation
recurrent convolutional neural network (RCNN)
Robustness
Simultaneous localization and mapping
simultaneous localization and mapping (SLAM)
Three-dimensional displays
Tracking
Training
unsupervised deep learning (DL)
Visualization
ISSN:0278-0046, 1557-9948
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Zusammenfassung:In this article, we propose DeepSLAM, a novel unsupervised deep learning based visual simultaneous localization and mapping (SLAM) system. The DeepSLAM training is fully unsupervised since it only requires stereo imagery instead of annotating ground-truth poses. Its testing takes a monocular image sequence as the input. Therefore, it is a monocular SLAM paradigm. DeepSLAM consists of several essential components, including Mapping-Net, Tracking-Net, Loop-Net, and a graph optimization unit. Specifically, the Mapping-Net is an encoder and decoder architecture for describing the 3-D structure of environment, whereas the Tracking-Net is a recurrent convolutional neural network architecture for capturing the camera motion. The Loop-Net is a pretrained binary classifier for detecting loop closures. DeepSLAM can simultaneously generate pose estimate, depth map, and outlier rejection mask. In this article, we evaluate its performance on various datasets, and find that DeepSLAM achieves good performance in terms of pose estimation accuracy, and is robust in some challenging scenes.
Bibliographie:ObjectType-Article-1
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2020.2982096