Autonomous ground refuelling approach for civil aircrafts using computer vision and robotics

3D visual servoing systems need to detect the object and its pose in order to perform. As a result accurate, fast object detection and pose estimation play a vital role. Most visual servoing methods use low-level object detection and pose estimation algorithms. However, many approaches detect objects in 2D RGB sequences for servoing, which lacks reliability when estimating the object’s pose in 3D space. To cope with these problems, firstly, a joint feature extractor is employed to fuse the object’s 2D RGB image and 3D point cloud data. At this point, a novel method called PosEst is proposed to exploit the correlation between 2D and 3D features. Here are the results of the custom model using test data; precision: 0,9756, recall: 0.9876, F1 Score(beta=1): 0.9815, F1 Score(beta=2): 0.9779. The method used in this study can be easily implemented to 3D grasping and 3D tracking problems to make the solutions faster and more accurate. In a period where electric vehicles and autonomous systems are gradually becoming a part of our lives, this study offers a safer, more efficient and more comfortable environment.