We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
一种多无人机协同定位与稠密地图构建算法.
- Authors
唐嘉宁; 陈伟; 陈云浩; 李玉亭; 胡敏森; 许俊锋
- Abstract
Aiming at the problem that the VINS-MONO (monoculor visual-inertial state) algorithm has low positioning accuracy and the sparse point cloud map constructed contains less information and cannot be used for autonomous navigation of unmanned aerial vehicles, a centralized multi-UAV (unmanned aerial vehicle) collaborative positioning and dense map construction algorithm was proposed. In order to establish the connection constraint between multiple UAVs, the algorithm used four different types of coordinate systems for coordinate transformation optimization, and used the two-way detection matching method to match the feature points of key frames, combined with the PROSAC (progressive sampling consensus) algorithm to eliminate false matching. By optimizing the global pose map of multiple UAVs with loopback closure constraints, a global dense map for collaborative navigation of five UAVs was constructed by combining Voxblox. In the five sequences of the EuRoc dataset, the proposed multi-UAV co-localization and dense map construction algorithm reduced the absolute trajectory error by 34%, 26%, 32%, 24% and 19% compared with the VINS-MONO algorithm, respectively. It is proved by experiments that the improved algorithm effectively improves the positioning accuracy between multiple UAVs, and the constructed globally consistent dense map contains distance information and gradient information, which can be used for autonomous navigation of multiple UAVs.
- Publication
Science Technology & Engineering, 2023, Vol 23, Issue 35, p15124
- ISSN
1671-1815
- Publication type
Article