DeepAVO: Efficient Pose Refining with Feature Distilling for Deep Visual Odometry

• URL: http://arxiv.org/abs/2105.09899v1
• Date: Thu, 20 May 2021 17:05:31 GMT
• Title: DeepAVO: Efficient Pose Refining with Feature Distilling for Deep Visual Odometry
• Authors: Ran Zhu, Mingkun Yang, Wang Liu, Rujun Song, Bo Yan, Zhuoling Xiao
• Abstract summary: This paper studies monocular Visual Odometry (VO) from the perspective of Deep Learning (DL) We present a novel four-branch network to learn the rotation and translation by leveraging Conal Neural Networks (CNNs) to focus on different quadrants of optical flow input. Experiments on various datasets involving outdoor driving and indoor walking scenarios show that the proposed DeepAVO outperforms the state-of-the-art monocular methods by a large margin.
• Score: 8.114855695727003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The technology for Visual Odometry (VO) that estimates the position and orientation of the moving object through analyzing the image sequences captured by on-board cameras, has been well investigated with the rising interest in autonomous driving. This paper studies monocular VO from the perspective of Deep Learning (DL). Unlike most current learning-based methods, our approach, called DeepAVO, is established on the intuition that features contribute discriminately to different motion patterns. Specifically, we present a novel four-branch network to learn the rotation and translation by leveraging Convolutional Neural Networks (CNNs) to focus on different quadrants of optical flow input. To enhance the ability of feature selection, we further introduce an effective channel-spatial attention mechanism to force each branch to explicitly distill related information for specific Frame to Frame (F2F) motion estimation. Experiments on various datasets involving outdoor driving and indoor walking scenarios show that the proposed DeepAVO outperforms the state-of-the-art monocular methods by a large margin, demonstrating competitive performance to the stereo VO algorithm and verifying promising potential for generalization.

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