TY - GEN
T1 - Comparative Assessment of Image Processing Algorithms for the Pose Estimation of Uncooperative Spacecraft
AU - Pasqualetto Cassinis, Lorenzo
AU - Fonod, R.
AU - Gill, Eberhard
AU - Ahrns, Ingo
AU - Gil Fernandez, Jesus
N1 - Conference code: 10
PY - 2019/8/17
Y1 - 2019/8/17
N2 - This paper reports on a comparative assessment of Image Processing (IP) tech- niques for the relative pose estimation of uncooperative spacecraft with a monocular camera. Currently, keypoints-based algorithms suffer from partial occlusion of the target, as well as from the different illumination conditions be- tween the required offline database and the query space image. Besides, al- gorithms based on corners/edges detection are highly sensitive to adverse il- lumination conditions in orbit. An evaluation of the critical aspects of these two methods is provided with the aim of comparing their performance under changing illumination conditions and varying views between the camera and the target. Five different keypoints-based methods are compared to assess the robustness of feature matching. Furthermore, a method based on corners ex- traction from the lines detected by the Hough Transform is proposed and evalu- ated. Finally, a novel method, based on an hourglass Convolutional Neural Net- work (CNN) architecture, is proposed to improve the robustness of the IP during partial occlusion of the target as well as during feature tracking. It is expected that the results of this work will help assessing the robustness of keypoints- based, corners/edges-based, and CNN-based algorithms within the IP prior to the relative pose estimation.
AB - This paper reports on a comparative assessment of Image Processing (IP) tech- niques for the relative pose estimation of uncooperative spacecraft with a monocular camera. Currently, keypoints-based algorithms suffer from partial occlusion of the target, as well as from the different illumination conditions be- tween the required offline database and the query space image. Besides, al- gorithms based on corners/edges detection are highly sensitive to adverse il- lumination conditions in orbit. An evaluation of the critical aspects of these two methods is provided with the aim of comparing their performance under changing illumination conditions and varying views between the camera and the target. Five different keypoints-based methods are compared to assess the robustness of feature matching. Furthermore, a method based on corners ex- traction from the lines detected by the Hough Transform is proposed and evalu- ated. Finally, a novel method, based on an hourglass Convolutional Neural Net- work (CNN) architecture, is proposed to improve the robustness of the IP during partial occlusion of the target as well as during feature tracking. It is expected that the results of this work will help assessing the robustness of keypoints- based, corners/edges-based, and CNN-based algorithms within the IP prior to the relative pose estimation.
M3 - Conference contribution
BT - International Workshop on Satellite Constellations and Formation Flying
CY - Glasgow, UK
T2 - IWSCFF 2019: 10th International Workshop on Satellite Constellations and Formation Flying
Y2 - 16 July 2019 through 19 July 2019
ER -