Improving Monocular Visual Odometry Using Learned Depth-Reference-Cited by-同舟云学术

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Improving Monocular Visual Odometry Using Learned Depth

Published:2022-10 Issue:5 Volume:38 Page:3173-3186
ISSN:1552-3098
Container-title:IEEE Transactions on Robotics
language:
Short-container-title:IEEE Trans. Robot.

Author:

Sun Libo¹^ORCID,Yin Wei¹^ORCID,Xie Enze²^ORCID,Li Zhengrong³,Sun Changming⁴^ORCID,Shen Chunhua⁵^ORCID

Affiliation:

1. School of Computer Science, The University of Adelaide, Adelaide, SA, Australia

2. Computer Science, The University of Hong Kong, Hong Kong

3. Northwest A&F University, Xianyang, China

4. Data61, CSIRO, Epping, NSW, Australia

5. Zhejiang University, Hangzhou, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/8860/9910236/09823399.pdf?arnumber=9823399

Reference77 articles.

1. Are we ready for autonomous driving? The KITTI vision benchmark suite

2. ImageNet: A large-scale hierarchical image database

3. Aggregated Residual Transformations for Deep Neural Networks

4. PyTorch: An imperative style, high-performance deep learning library;paszke;Proc Adv Neural Inf Process Syst,0

5. An overview of neural network compression;neill;Comput Res Repository,2020

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3. DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment With Multiple Sensors for Large-Scale Localization and Mapping;IEEE Robotics and Automation Letters;2024-07

4. MD2VO: Enhancing Monocular Visual Odometry through Minimum Depth Difference;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

5. MS-VRO: A Multistage Visual-Millimeter Wave Radar Fusion Odometry;IEEE Transactions on Robotics;2024

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