@inproceedings{a0134ed3f201490784ea2d2c67c5c189,
title = "A Visual Localization System for Complex Indoor Environment",
abstract = "In recent years, unmanned technology has been widely used in military and civil fields. However, agents often stop working because of positioning problems in complex urban environments. In this paper, we present a real-time Visual Localization System for complex indoor scenes. we describe the problem of state estimation in visual localization. The essence of it is to obtain the maximum likelihood estimation of the state variables from the camera observation data. In our System, we use ORB feature to estimate the motion of the camera between adjacent frames after balancing the accuracy and efficiency of system. We also maintain a local map in the system and use nonlinear optimization to further optimize the estimation results. Through the test of TUM RGB-D benchmark, the system can operate in real time with 21FPS. The results show that the triaxial position error is about 0.03 m, and the attitude error is 0.06 rad.",
keywords = "Nonlinear optimization, Slam, Visual localization, Visual odometry",
author = "Jiang, \{X. L.\} and Liu, \{Chao Yang\} and Hongbin Deng",
note = "Publisher Copyright: {\textcopyright} 2023, Beijing HIWING Sci. and Tech. Info Inst.; International Conference on Autonomous Unmanned Systems, ICAUS 2022 ; Conference date: 23-09-2022 Through 25-09-2022",
year = "2023",
doi = "10.1007/978-981-99-0479-2\_136",
language = "English",
isbn = "9789819904785",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "1482--1492",
editor = "Wenxing Fu and Mancang Gu and Yifeng Niu",
booktitle = "Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022",
address = "Germany",
}