Optical Flow Assisted Monocular Visual Odometry | |
Wan, Yiming1,2; Gao, Wei1,2; Wu, Yihong1,2 | |
2020-02 | |
会议日期 | 2019.11.26-2019.11.29 |
会议地点 | Auckland, New Zealand |
英文摘要 | This paper proposes a novel deep learning based approach for monocular visual odometry (VO) called FlowVO-Net. Our approach utilizes CNN to extract motion information between two consecutive frames and employs Bi-directional convolution LSTM (Bi-ConvLSTM) for temporal modelling. ConvLSTM can encode not only temporal information but also spatial correlation, and the bidirectional architecture enables it to learn the geometric relationship from image sequences pre and post. Besides, our approach jointly predicts optical flow as an auxiliary task in a self-supervised way by measuring photometric consistency. Experiment results indicate competitive performance of the proposed FlowVO-Net to the state-of-art methods. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/39135] |
专题 | 自动化研究所_模式识别国家重点实验室_机器人视觉团队 |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
推荐引用方式 GB/T 7714 | Wan, Yiming,Gao, Wei,Wu, Yihong. Optical Flow Assisted Monocular Visual Odometry[C]. 见:. Auckland, New Zealand. 2019.11.26-2019.11.29. |
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