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
Online-Zugang:	Volltext
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