Paper Title
EFFECTIVE LANDMARK REGRESSION USING ATTENTION BASED-HRNET FOR SATELLITE POSE ESTIMATION

Abstract
For many space missions, it is important to estimate the position and orientation of the satellite for operations such as docking and debris removal. It involves the following stages of object detection, landmark regression and pose estimation. For objection detection, we used Faster-RCNN with HRNet as the backbone, the landmark regression part is done using AHRNet architecture, the pose estimation is implemented using PnP algorithm. Firstly, each image was labeled for object detection with bounding boxes around the satellite images created in Blender which were then used to train for satellite detection. An AHRNet was further trained for landmark regression using a 4fold cross-validation approach which involved splitting the dataset into multiple training and validation sets to enhance the Intersection over Union (IoU) metric. After the landmark regression provided a 2D projection of 3D ground truth points, the PnP algorithm was then used for pose estimation. To improve pose estimation accuracy, we integrated solvePnP with an iterator argument utilizing the Levenberg-Marquardt (LM) method to reduce noise and outliers. Our methodology significantly enhances the precision and efficiency of the translation and rotation error of the satellite during the docking processes offering a viable solution for autonomous space missions with potential for future improvements in domain adaptability through the development of unsupervised domain adaptation models. The results of the landmark regression using AHRNet shows an improved reprojection error, orientation and translation errors. Keywords - Satellite Pose Estimation, AHRNet, Landmark Regression, Pose Estimation