| Author: | Luo, Haojun |
| Title: | A low-cost relative positioning method for UAV/UGV coordinated heterogeneous system based on visual-lidar fusion |
| Advisors: | Wen, Chih-yung (ME) |
| Degree: | M.Sc. |
| Year: | 2023 |
| Subject: | Automated vehicles Drone aircraft Navigation Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Mechanical Engineering |
| Pages: | 43 pages : color illustrations |
| Language: | English |
| Abstract: | This thesis presents a low-cost relative positioning method for a ground-air configuration consisting of Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs). The proposed method leverages the fusion of visual and Light Detection and Ranging (LiDAR) data to enhance the reliability, adaptability, and robustness of the system. Specifically, the system incorporates several key components, including the YOLO (You Only Look Once) object detection algorithm, LiDAR inertial odometry, and Kalman filter tracking algorithm, each contributing to the overall effectiveness of the relative positioning method. The system integrates a camera and a LiDAR sensor, both mounted on the UGV, after being accurately calibrated. The camera utilizes the YOLO object detection algorithm to detect and obtain the three-dimensional (3D) position of the UAV in a visual manner, leveraging depth information from a depth camera. The LiDAR sensor directly obtains the 3D position of the UAV with the aid of visual detection. To track the 3D position of the UAV in the UGV coordinate system, a Kalman filter is employed to fuse the position information from the visual and LiDAR sensors. Through the utilization of localization methods (motion capture system VICON and LiDAR inertial odometry), the UGV can autonomously move and dynamically follow the UAV without requiring human interaction. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 7473.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.4 MB | Adobe PDF | View/Open |
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