Comparative Assessment of Image Processing Algorithms for the Pose Estimation of Uncooperative Spacecraft

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.

[1]  Xiaowei Zhou,et al.  6-DoF object pose from semantic keypoints , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[2]  John Leif Jørgensen,et al.  Pose estimation of an uncooperative spacecraft from actual space imagery , 2014 .

[3]  Eberhard Gill,et al.  Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft , 2019, Progress in Aerospace Sciences.

[4]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[5]  Soon-Jo Chung,et al.  Robust Features Extraction for On-board Monocular-based Spacecraft Pose Acquisition , 2019, AIAA Scitech 2019 Forum.

[6]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[7]  Nabil Aouf,et al.  Multi-view monocular pose estimation for spacecraft relative navigation , 2018 .

[8]  Simone D'Amico,et al.  Robust Model-Based Monocular Pose Initialization for Noncooperative Spacecraft Rendezvous , 2018, Journal of Spacecraft and Rockets.

[9]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[10]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[11]  Wenfu Xu,et al.  Pose measurement of large non-cooperative satellite based on collaborative cameras , 2011 .

[12]  Jian-Feng Shi,et al.  Spacecraft Pose Estimation Using a Monocular Camera , 2019 .

[13]  Nabil Aouf,et al.  Multispectral Image Processing for Navigation Using Low Performance Computing , 2018 .

[14]  Vincent Lepetit,et al.  Monocular Model-Based 3D Tracking of Rigid Objects: A Survey , 2005, Found. Trends Comput. Graph. Vis..

[15]  Richard I. Hartley,et al.  In Defense of the Eight-Point Algorithm , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[17]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[18]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.