@inproceedings{6659e01ef0dc448eb0cc7b678c80c3fd,
title = "VESC-SLAM: A Visual Information Enhanced Multi-Robot Synchronous Collaborative SLAM",
abstract = "The collaborative Simultaneous Localization and Mapping (SLAM) system between unmanned aerial vehicles and unmanned ground vehicles in low-light environments faces the problem of poor quality such as blurred and missing visual information. This study constructs a collaborative SLAM system based on deep neural networks to solve this problem. The system significantly i mproves t he p ositioning a ccuracy and map construction quality of unmanned aerial vehicles and unmanned ground vehicles under low-light conditions through the cooperation of the front-end image enhancement module, the collaborative SLAM core processing module, and the intelligent data collaboration and resource optimization module. The system has been validated on the Euroc dataset processed by darkening and its performance has been compared with other robot cluster collaborative SLAM.",
keywords = "collaborative simultaneous localization and mapping, deep neural network, low-light, unmanned aerial vehicles, unmanned ground vehicles",
author = "Jingyu Yu and Longbo Cheng and Jianshan Zhou and Xuting Duan and Chenghao Ren and Ken Chen",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Conference on Unmanned Systems, ICUS 2024 ; Conference date: 18-10-2024 Through 20-10-2024",
year = "2024",
doi = "10.1109/ICUS61736.2024.10839820",
language = "英语",
series = "Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1246--1252",
editor = "Rong Song",
booktitle = "Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024",
address = "美国",
}