A Highly Integrated Navigation Unit for On-Orbit Servicing Missions

VINAG (VISION/INS integrated Navigation Assisted by GNSS) is a highly integrated multisensor navigation unit, particularly conceived for On-Orbit Servicing missions. The system is designed to provide all-in-one, on-board real time autonomous absolute navigation as well as pose determination of an uncooperative known object orbiting in LEO (Low Earth Orbit), GEO (GEosynchronous Orbits) and possibly in HEO (Highly Earth Orbit). The system VINAG is under development by a team of Italian companies and universities, co-financed by the Italian Space Agency. Thanks to a tight optimized integration of its subsystems, VINAG is characterized by a low power and mass total budgets and therefore it is suitable for small and very small satellites. In order to provide both 1) absolute orbit and attitude determination and 2) vision-based pose determination, the unit integrates three metrology systems: a Cameras Subsystem (a monocular camera and a Star sensor), an Inertial Measurement Unit (IMU) and a GNSS (Global Navigation Satellite System) receiver. In this paper, we briefly introduce the complete system architecture, the adopted algorithms and then we detail the adopted hardware design solutions. In addition, we describe preliminary numerical simulation results obtained for different orbits from LEO to GEO carried out for the validation phase of VINAG.

[1]  Antonio Pedro Aguiar,et al.  Second-Order-Optimal Minimum-Energy Filters on Lie Groups , 2016, IEEE Transactions on Automatic Control.

[2]  F. Markley Attitude Error Representations for Kalman Filtering , 2003 .

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

[4]  Robert E. Mahony,et al.  Nonlinear Attitude Filtering: A Comparison Study , 2015, ArXiv.

[5]  J. Kittler,et al.  Comparative study of Hough Transform methods for circle finding , 1990, Image Vis. Comput..

[6]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[7]  Christopher D'Souza,et al.  Absolute Navigation Performance of the Orion Exploration Fight Test 1 , 2017 .

[8]  John L. Crassidis,et al.  Geometric Integration of Quaternions , 2012 .

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

[10]  Michèle Lavagna,et al.  Stereovision-based pose and inertia estimation of unknown and uncooperative space objects , 2017 .

[11]  D. Simon Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[12]  Michael M. Madden Gravity Modeling for Variable Fidelity Environments , 2006 .

[13]  Manolis I. A. Lourakis,et al.  Model-Based Pose Estimation for Rigid Objects , 2013, ICVS.

[14]  Philip David,et al.  SoftPOSIT: Simultaneous Pose and Correspondence Determination , 2002, ECCV.

[15]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[16]  Rudolph van der Merwe,et al.  The square-root unscented Kalman filter for state and parameter-estimation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[17]  Sumant Sharma,et al.  Comparative assessment of techniques for initial pose estimation using monocular vision , 2016 .

[18]  V. Lepetit,et al.  EPnP: An Accurate O(n) Solution to the PnP Problem , 2009, International Journal of Computer Vision.

[19]  K. Yamanaka,et al.  New State Transition Matrix for Relative Motion on an Arbitrary Elliptical Orbit , 2002 .

[20]  Baiqing Hu,et al.  Modified Unscented Quaternion Estimator Based on Quaternion Averaging , 2014 .

[21]  Oliver Montenbruck,et al.  Satellite Orbits: Models, Methods and Applications , 2000 .