Self-Supervised Deep Visual Odometry Based on Geometric Attention Model

Author:

Dai Jiajia1,Gong Xiaoxi1ORCID,Li Yida1ORCID,Wang Jun1ORCID,Wei Mingqiang2

Affiliation:

1. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Key Research and Development Program of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Self-supervised monocular visual odometry based on cross-correlation;Measurement Science and Technology;2024-05-13

2. Lite-SVO: Towards A Lightweight Self-Supervised Semantic Visual Odometry Exploiting Multi-Feature Sharing Architecture;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

3. Deep Learning for Visual Localization and Mapping: A Survey;IEEE Transactions on Neural Networks and Learning Systems;2023

4. Efficient Deep-Learning 4D Automotive Radar Odometry Method;IEEE Transactions on Intelligent Vehicles;2023

5. Pseudo-LiDAR for Visual Odometry;IEEE Transactions on Instrumentation and Measurement;2023

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