@inproceedings{f364477c8f144c98aa503f0f9e8b6145,
title = "Real-Time Dense Visual Odometry for RGB-D Cameras",
abstract = "We propose a visual-inertial localization and mapping (SLAM) algorithm that is lightweight and robust by using an RGB-D camera. It can achieve real-time six-degree-of-freedom pose estimation and dense mapping. In this algorithm, recognizing and filtering dynamic features is added, which will reduce the influence that moving objects incorporate and render pose evaluation more robust. In addition, this approach exploits batch point-to-point and point-to-plane constraints to refine the motion evaluation results, and restrain the cumulative error. We validated the performance of this algorithm on datasets and real-world experiments against other state-of-the-art methods. Finally, we applied the algorithm to an aerial vehicle in GPS-denied environments to accomplish the specified mission and verified the reliability of the algorithm in scenarios with vibration.",
keywords = "Dense mapping, GPS-denied environments, Visual odometry",
author = "Baozhen Nie and Yingxun Wang and Jiang Zhao and Zhihao Cai and Chiyu Cao",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Guidance, Navigation and Control, ICGNC 2022 ; Conference date: 05-08-2022 Through 07-08-2022",
year = "2023",
doi = "10.1007/978-981-19-6613-2\_504",
language = "英语",
isbn = "9789811966125",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "5221--5232",
editor = "Liang Yan and Haibin Duan and Yimin Deng and Liang Yan",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control",
address = "德国",
}