Robust vision-based pose estimation of moving objects for Automated Rendezvous & Docking

Abstract

This paper presents a fault-tolerant method for pose estimation of space objects using 3-D vision data by integration of a Kalman filter (KF) and an Iterative Closest Point (ICP) algorithm in a closed-loop configuration. The initial guess for the internal ICP iteration is provided by state estimate propagation of the Kalman filer. The Kalman filter is capable of not only estimating the target's states, but also its inertial parameters. This allows the motion of target to be predictable as soon as the filter converges. Consequently, the ICP can maintain pose tracking over a wider range of velocity due to increased precision of ICP initialization. Furthermore, incorporation of the target's dynamics model in the estimation process allows the estimator continuously provide pose estimation even when the sensor temporally loses its signal namely due to obstruction. The capabilities of the pose estimation methodology is demonstrated by a ground testbed for Automated Rendezvous & Docking (AR&D). In this experiment, Neptec's Laser Camera System (LCS) is used for real-time scanning of a satellite model attached to a manipulator arm, which is driven by a simulator according to orbital and attitude dynamics. The results showed that robust tracking of the free-floating tumbling satellite can be achieved only if the Kalman filter and ICP are in closed-loop configuration.

3D pose estimation of ground rigid target based on ladar range image

“…The model pose with the highest score represents the measured target pose. The features for pose estimation include point [12,13], surface and contour information [14], local shape descriptors [15], viewpoint feature histogram [16], and clustered viewpoint feature histogram [17]. The feature-based matching method is usually applicable to the known target.…”

Relative pose estimation of satellites using PMD-/CCD-sensor data fusion

“…Pose estimation, in a more general context, can be solved by different sensors and methods, such as Iterative Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/actaastro Closest Point (ICP) using laser scanners [6], neural networks [13], monocular cue processing with cameras [14] and more. A non-exhaustive overview is given in [15].…”

“…When it comes to pose estimation and relative navigation, usually laser scanners and stereo vision are the sensor types used in terrestrial and space applications. While laser scanners are robust against lighting changes [6] and are often used for autonomous docking with reflectors [7], stereo vision approaches provide measurements of high accuracy at a high rate, at the price of becoming reliant on the environmental light and often, the computational requirements increase as well [8].…”

Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression

“…Optical imaging sensors have been widely used as the essential payloads of vision systems in aerospace applications: autonomous rendezvous and docking [1][2][3][4], vision-based landing [5], position and pose estimation [6][7][8][9][10][11][12][13], on-orbit serving [14,15], space robotics [16], satellite recognition [12,[17][18][19], 3D structure reconstruction and component detection [20,21], etc. Vision-based recognition and pose estimation of a target satellite are one of the key technologies to achieve these applications.…”