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Using Unsupervised Deep Learning Technique for Monocular Visual Odometry

Liu, Qiang and Li, Ruihao and Hu, Huosheng and Gu, Dongbing (2019) 'Using Unsupervised Deep Learning Technique for Monocular Visual Odometry.' IEEE Access, 7. 18076 - 18088. ISSN 2169-3536

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Deep learning technique-based visual odometry systems have recently shown promising results compared to feature matching-based methods. However, deep learning-based systems still require the ground truth poses for training and the additional knowledge to obtain absolute scale from monocular images for reconstruction. To address these issues, this paper presents a novel visual odometry system based on a recurrent convolutional neural network. The system employs an unsupervised end-to-end training approach. The depth information of scenes is used alongside monocular images to train the network in order to inject scale. Poses are inferred only from monocular images, thus making the proposed visual odometry system a monocular one. The experiments are conducted and the results show that the proposed method performs better than other monocular visual odometry systems. This paper has made two main contributions: 1) the creation of the unsupervised training framework in which the camera ground truth poses are only deployed for system performance evaluation rather than for training and 2) the absolute scale could be recovered without the post-processing of poses.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 11 Mar 2019 10:12
Last Modified: 11 Mar 2019 10:12

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