Name
Abstract | ||
|---|---|---|
In this paper, we present a method that uses the theory of maximum likelihood estimation to improve the precision of the unmanned aerial vehicle (UAV) localization algorithm based on monocular vision. The main goal of this work is to obtain the accurate position information of UAV and achieve the autonomous navigation in complex indoor and outdoor environments. An embedded camera mounted on the UAV platform is used to provide real-time video streams to the vision-based localization algorithm. All the algorithms run in the onboard computer to ensure the real-time property of the system. Simulation of the UAV platform with monocular camera is performed to verify the feasibility of the improved localization algorithm firstly. After the simulation verification, a series of real-time experiments are implemented to demonstrate the precision of the algorithm. |
Year | Venue | Field |
|---|---|---|
2017 | 2017 IEEE INTERNATIONAL CONFERENCE ON REAL-TIME COMPUTING AND ROBOTICS (RCAR) | Monocular vision,Computer science,Maximum likelihood,Algorithm,Feature extraction,Monocular camera,Simultaneous localization and mapping |
DocType | ISBN | Citations |
Conference | 978-1-5386-2035-9 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shiyu Chen | 1 | 33 | 11.44 |
| Yanjie Li | 2 | 41 | 8.99 |
| Haoyao Chen | 3 | 189 | 23.79 |