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• W3130443363 abstract "In this paper, we study deep learning approaches for monocular visual odometry (VO). Deep learning solutions have shown to be effective in VO applications, replacing the need for highly engineered steps, such as feature extraction and outlier rejection in a traditional pipeline. We propose a new architecture combining ego-motion estimation and sequence-based learning using deep neural networks. We estimate camera motion from optical flow using Convolutional Neural Networks (CNNs) and model the motion dynamics using Recurrent Neural Networks (RNNs). The network outputs the relative 6-DOF camera poses for a sequence, and implicitly learns the absolute scale without the need for camera intrinsics. The entire trajectory is then integrated without any post-calibration. We evaluate the proposed method on the KITTI dataset and compare it with traditional and other deep learning approaches in the literature." @default.
• W3130443363 created "2021-03-01" @default.
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• W3130443363 date "2021-02-12" @default.
• W3130443363 modified "2023-09-26" @default.
• W3130443363 title "Leveraging Deep Learning for Visual Odometry Using Optical Flow" @default.
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• W3130443363 doi "https://doi.org/10.3390/s21041313" @default.
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