A dataset to support and benchmark computer vision development for close range on-orbit servicing

This paper presents the first publicly available dataset for Close Range On-Orbit Servicing Computer Vision (CROOS-CV) intended for testing and benchmarking of computer vision algorithms. It is an representative image dataset for CROOS operations with distances of 2 m between servicer and client satellite that was recorded under illumination conditions similar to a Low Earth Orbit. A training set with 180 trajectories and a test set with 810 trajectories are provided. Both were recorded at three different sun incidence angles and with multiple different shutter times. Each trajectory consist of stereo image pairs along with the ground truth pose of the cameras. Additionally, a 3D model of the client and all calibration data is provided with the dataset. The paper provides details about the recording setup, the calibration and recording procedure. Results from tests with a visual tracking algorithm are provided. The dataset is available online at http://rmc.dlr.de/rm/en/staff/martin.lingenauber/crooscv-dataset.

[1]  Gerd Hirzinger,et al.  More accurate camera and hand-eye calibrations with unknown grid pattern dimensions , 2008, 2008 IEEE International Conference on Robotics and Automation.

[2]  Steven M. LaValle,et al.  RRT-connect: An efficient approach to single-query path planning , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[3]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..

[4]  K. Schilling,et al.  FORROST: Advances in on-orbit robotic technologies , 2015, 2015 IEEE Aerospace Conference.

[5]  Richard Szeliski,et al.  A Database and Evaluation Methodology for Optical Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[6]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Roberto Cipolla,et al.  Real-time tracking of complex structures with on-line camera calibration , 2002, Image Vis. Comput..

[8]  Gino van den Bergen.  Collision Detection in Interactive 3D Environments , 2003 .

[9]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Edwin Olson,et al.  AprilTag: A robust and flexible visual fiducial system , 2011, 2011 IEEE International Conference on Robotics and Automation.

[11]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Michael Suppa,et al.  Efficient next-best-scan planning for autonomous 3D surface reconstruction of unknown objects , 2013, Journal of Real-Time Image Processing.

[13]  Nassir Navab,et al.  Edge-Based Template Matching and Tracking for Perspectively Distorted Planar Objects , 2008, ISVC.

[14]  Giorgio Panin,et al.  Model-based Visual Tracking: The OpenTL Framework , 2011 .

[15]  J. J. Moré,et al.  Levenberg--Marquardt algorithm: implementation and theory , 1977 .

[16]  Patrick Bouthemy,et al.  A 2D-3D model-based approach to real-time visual tracking , 2001, Image Vis. Comput..

[17]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.