@inproceedings{7d34aa8c11404dd9b310e6400bcb6fd5,
title = "Feature Point Fusion Strategy Based on Visual and LIDAR Information",
abstract = "Visual simultaneous localization and mapping (SLAM) sys-tems still have limitation in application due to the range-limited depth estimation of visual cameras in large-scale motion-distance scenes. The accurate depth estimation capability of LiDAR in 3D scenes makes it adventageous to compensate the defects of camera. This study designs a novel strategy to correlate the visual ORB feature points with the corre-sponding LiDAR depth information so as to generate the fused feature points. Based on this fusion strategy, the Visual/LiDAR/Inertial Mea-surement Unit (IMU)-based ORB-SLAM3 is further developed, which extends the applicability of the original ORB-SLAM3. Finally, the pro-posedSLAMsystemisevaluated basedonopensourcedatasetsinterms of the stability of the fused feature points and the localization accuracy of the extended system using the fused feature points.",
keywords = "Depth estimation, Fusion feature points, ORB-SLAM3 expansion, Visual-LiDAR fusion",
author = "Shijie Wu and Haoyu Qi and Yuhang Zhang and Zhen Li and Wenjie Chen",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; International Conference on Guidance, Navigation and Control, ICGNC 2024 ; Conference date: 09-08-2024 Through 11-08-2024",
year = "2025",
doi = "10.1007/978-981-96-2228-3\_19",
language = "English",
isbn = "9789819622276",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "206--216",
editor = "Liang Yan and Haibin Duan and Yimin Deng",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 8",
address = "Germany",
}