A Computationally Efficient Visual SLAM for Low-light and Low-texture Environments Based on Neural Networks-Reference-Cited by-同舟云学术

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A Computationally Efficient Visual SLAM for Low-light and Low-texture Environments Based on Neural Networks

Published:2023-07-17 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Real-time Computing and Robotics (RCAR)
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Author:

Zhao Xuhui¹,Zhao Anqi¹,Liao Zhenghao¹,Huang Ziqian¹,Gao Zhi¹

Affiliation:

1. Wuhan University,School of Remote Sensing and Information Engineering,Wuhan,China,430079

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10249208/10249018/10249872.pdf?arnumber=10249872

Reference42 articles.

1. SuperGlue: Learning Feature Matching With Graph Neural Networks

2. Parallel and Pipelining design of SLAM Feature Detection Algorithm in Hardware

3. GCNv2: Efficient Correspondence Prediction for Real-Time SLAM

4. A fully integrated system for hardware-accelerated TSDF SLAM with LiDAR sensors (HATSDF SLAM)

5. PL-SLAM: A Stereo SLAM System Through the Combination of Points and Line Segments

Cited by 1 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. A Robust and Efficient Visual-Inertial SLAM for Vision-Degraded Environments;2024 IEEE 18th International Conference on Control & Automation (ICCA);2024-06-18

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