@inproceedings{7346ac3c37ea49489ae183a81509c315,
title = "Graph Optimization based Visual SLAM fusing KeyPoints and Markers",
abstract = "Simultaneous localization and mapping (SLAM) plays an important role in autonomous navigation for mobile robots. Most of the visual SLAM approaches use keypoints for tracking, whose performance however suffers from the unstable landmarks during task due to uncertain light condition and frequently changeable viewpoint. The situation even becomes worse for visual SLAM in low texture environment especially in indoor buildings, where the supplementary artificial markers can be used to improve the robust detection under a wider range of circumstances. Inspired by this thought, this paper developed a visual SLAM system integrated with keypoints and artificial markers. A graph optimization problem is constructed to optimize the trajectory by taking both the reprojection error of keypoints and the influence of markers into consideration. The experimental results on SPM dataset demonstrate the superior accuracy of the proposed graph optimization algorithm compared with the start-of-the-art ORB-SLAM2.",
keywords = "Visual simultaneous localization and mapping (SLAM), artificial markers, graph optimization, keypoints",
author = "Zhen Chen and Yang Zhou and Fengdi Zhang and Min Xu and Xiangdong Liu and Zhen Li",
note = "Publisher Copyright: {\textcopyright} 2020 Technical Committee on Control Theory, Chinese Association of Automation.; 39th Chinese Control Conference, CCC 2020 ; Conference date: 27-07-2020 Through 29-07-2020",
year = "2020",
month = jul,
doi = "10.23919/CCC50068.2020.9188813",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "3483--3488",
editor = "Jun Fu and Jian Sun",
booktitle = "Proceedings of the 39th Chinese Control Conference, CCC 2020",
address = "United States",
}