Robust Model-Based Monocular Pose Initialization for Noncooperative Spacecraft Rendezvous

Abstract

This work addresses the design and validation of a robust monocular vision-based pose initialization architecture for close-range onorbit-servicing and formation-flying applications. The aim is to rapidly determine the pose of a passive space resident object using its known three-dimensional wireframe model and a single low-resolution two-dimensional image collected on board the servicer spacecraft. In contrast to previous works, the proposed architecture is onboard executable and capable of estimating the pose of the client without the use of fiducial markers and without any a priori range measurements or state information. A novel feature detection method based on the filtering of the weak image gradients is used to identify the true edges of the client in the image, even in presence of the Earth in background. The detected features are synthesized using simple geometric constraints to dramatically reduce the search space of the feature correspondence problem, which is solved using the EPnP method. This approach is proven to be an order of magnitude faster than the state-of-the-art random sample consensus methods. A fast Newton-Raphson method that minimizes the fit error between corresponding image and model features is employed to refine the pose estimate and to resolve pose ambiguity. The proposed methodology is tested using actual space imagery collected during the PRISMA mission at about a 700 km altitude and a 10 m interspacecraft separation.

CNN-Based Pose Estimation System for Close-Proximity Operations Around Uncooperative Spacecraft

“…For such scenarios, the estimation of the relative position and attitude (pose) of an uncooperative spacecraft by an active servicer spacecraft represents a critical task. Pose estimation systems based solely on a monocular camera are recently becoming an attractive alternative to systems based on active sensors or stereo cameras, due to their reduced mass, power consumption and system complexity [3]. However, given the low Signal-To-Noise Ratio (SNR) and the high contrast which characterize space images, a significant effort is still required to comply with most of the demanding requirements for a robust and accurate monocular-based navigation system.…”

“…The implementation of CNNs for monocular pose estimation in space has already become an attractive solution in recent years [10][11][12], also thanks to the creation of the Spacecraft PosE Estimation Dataset (SPEED) [11], a database of highly representative synthetic images of PRISMA's TANGO spacecraft made publicly available by Stanford's Space Rendezvous Laboratory (SLAB) applicable to train and test different network architectures. One of the main advantages of CNNs over standard feature-based algorithms for relative pose estimation [3,13,14] is an increase in the robustness under adverse illumination condition, as well as a reduction in the computational complexity. Since the pose accuracies of the first adopted CNNs proved to be lower than the accuracies returned by common pose estimation solvers, especially in the estimation of the relative attitude [10], recent efforts investigated the capability of CNNs to perform keypoint localization prior to the actual pose estimation [15][16][17][18].…”

Pose estimation for non-cooperative spacecraft rendezvous using convolutional neural networks

“…Prior demonstrations of close-range pose estimation have utilized image processing based on hand-engineered features [4][5][6][7][8][9][10][11][12] and an a-priori knowledge of the pose. [13][14][15][16] A key strength for many of these methods is their use of the perspective transformation between the scene and the image to hypothesize and test feature correspondences detected in the 2D image and the known 3D model of target spacecraft.…”

Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement

“…Existing solutions are mainly based on active sensor-based systems, e.g., the TriDAR system which uses LiDAR [12,28]. Recently, monocular pose estimation techniques for space applications are drawing significant attention due to their lower power consumption and relatively simple requirements [11,31,30,9].…”