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DeepSLAM: A Robust Monocular SLAM System with Unsupervised Deep Learning

Li, Ruihao and Wang, Sen and Gu, Dongbing (2021) 'DeepSLAM: A Robust Monocular SLAM System with Unsupervised Deep Learning.' IEEE Transactions on Industrial Electronics, 68 (4). pp. 3577-3587. ISSN 0278-0046

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In this paper, 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 3D structure of the environment while the Tracking-Net is a Recurrent Convolutional Neural Network (RCNN) architecture for capturing the camera motion. The Loop-Net is a pre-trained binary classifier for detecting loop closures. DeepSLAM can simultaneously generate pose estimate, depth map and outlier rejection mask. 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.

Item Type: Article
Uncontrolled Keywords: Simultaneous localization and mapping; Visualization; Training; Three-dimensional displays; Optimization; Pose estimation; Depth estimation; machine learning; recurrent convolutional neural network (RCNN); simultaneous localization and mapping (SLAM); unsupervised deep learning (DL)
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 13 May 2020 15:35
Last Modified: 14 Jan 2022 22:01

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