Uncooperative Spacecraft Relative Navigation With LIDAR-Based Unscented Kalman Filter

Abstract

Autonomous relative navigation is a critical functionality which needs to be developed to enable safe maneuvers of a servicing spacecraft (chaser) in close-proximity with respect to an uncooperative space target, in the frame of future On-Orbit Servicing or Active Debris Removal missions. Due to the uncooperative nature of the target, in these scenarios, relative navigation is carried out exploiting active or passive Electro-Optical sensors mounted on board the chaser. The focus here is placed on active systems, e.g., LIDARs. In this paper, an original loosely-coupled relative navigation architecture which integrates pose determination algorithms designed to process raw LIDAR data (i.e., 3D point clouds) within a Kalman filtering scheme is presented. Pose determination algorithms play a twofold role being used to initialize the filter state and covariance as well as in the update phase of the Kalman filter. The proposed filtering scheme is an Unscented Kalman Filter designed to use, as measurements for the update phase, relative position, attitude and angular velocity estimates. Performance assessment is carried out within a simulation environment realistically reproducing the operation of a scanning LIDAR and the relative motion between two spacecraft during a target monitoring maneuver. The numerical simulation campaign demonstrates robustness of the proposed approach even when dealing with challenging conditions (e.g., low range measurement accuracy, low update rate and high point-cloud sparseness) determined by the LIDAR noise level and operational parameters.

Lidar Pose Tracking of a Tumbling Spacecraft Using the Smoothed Normal Distribution Transform

“…For speeding up the tracking process, they combine the ICP with key-point description and matching. When using lidar simulators, motion blur is an effect which is often not modeled to generate the point clouds [36,40]. However, to account for low lidar frame-rates and the pose shift that might have happened between two scans, Opromolla and Nocerino use an unscented Kalman filter to predict the current pose of the target spacecraft [40].…”

“…When using lidar simulators, motion blur is an effect which is often not modeled to generate the point clouds [36,40]. However, to account for low lidar frame-rates and the pose shift that might have happened between two scans, Opromolla and Nocerino use an unscented Kalman filter to predict the current pose of the target spacecraft [40]. The unscented filter is preferred for accounting for the non-linearities of the dynamics.…”

“…When the lidar frame-rate is low, or the relative dynamics is fast, this initial guess might be inaccurate. A more precise initial estimate can be obtained by using the prediction from a navigation filter as an initial estimate, whether it is with a simple kinematic-only filter [36] or a more complex unscented filter [40]. Similarly to [36], we use a simple filter to estimate the current velocity and angular velocity of the target satellite.…”

Bridging the Domain Gap in Satellite Pose Estimation: A Self-Training Approach Based on Geometrical Constraints

“…E Stimating the pose of an uncooperative spacecraft is crucial in numerous space missions, such as debris removal [1], on-orbit servicing [2], and assets refueling [3]. In the last two decades, numerous relative navigation systems have been proposed based on LiDARs [4], [5] and cameras [6]- [11]. Active sensors, including radars and LiDARs, requires larger mass and higher power consumption compared to visual sensors.…”

Advanced motion estimations and predictions of a tumbling, non-cooperative space object during long-term occlusion

“…Besides the EKF-based approaches for the parameter and motion estimation problem, researchers have also implemented an advanced Kalman Filter technique called the Unscented Kalman Filter (UKF) which does not require the linearization of the non-linear dynamics systems and is generally more robust to system nonlinearity than EKF. [7][8][9] Additionally, apart from the Kalman Filter related techniques, several other physics model-based approaches have been studied, which include the non-linear least square technique, 10 smoothing and mapping factor-graph-based method, 11 constrained convex optimization method, 12 etc.…”