REAL-TIME ANGULAR VELOCITY ESTIMATION OF NON-COOPERATIVE SPACE OBJECTS USING CAMERA MEASUREMENTS Conference Paper uri icon

abstract

  • © 2018 Univelt Inc. All rights reserved. This paper presents an algorithm for angular velocity estimation of a non-cooperative space object using camera measurements. We consider that the non-cooperative space target is one whose inertia properties and actuation torques are not known. The relative pose of such space object with respect to the camera can be obtained using Simultaneous Localization and Mapping (SLAM) methods. In this paper, we specifically adopt the ORB-SLAM package, which has already been validated in prior research as a successful tool for SLAM applications in space missions. Using the relative pose between the target and the camera, the angular velocity can be obtained through attitude kinematics. However, the lack of a reliable propagation model for the angular velocity constrains the use of traditional Kalman Filter based methods, which typically require some knowledge of the inertia matrix and any perturbing torques governing the rotational dynamics of the non-cooperative space object. Instead, our work is based on the Discrete Adaptive Angular Velocity Estimator (DAAVE) algorithm to estimate for the target’s spin axis, and use this as prior information for a modified version of the Multiplicative Extended Kalman Filter (MEKF) formulation. This work introduces both the DAAVE and the modified MEKF algorithms, and presents the performance of the angular velocity estimator using a camera-target simulator. In our simulator, we are able to use the 3D model of a target of interest, which can be configured to tumble with any desired angular rate, while being visually captured with a camera. The simulation results demonstrate that the algorithm pipeline engagind ORB-SLAM, DAAVE, and the modified MEKF, is successful in adequately tracking the angular velocity of targets in multiple tumbling configurations.

author list (cited authors)

  • de Almeida, M. M., Zanetti, R., Mortari, D., & Akella, M.

publication date

  • January 2019