Robust On-Manifold Optimization for Uncooperative Space Relative Navigation with a Single Camera

Optical cameras are gaining popularity as the suitable sensor for relative navigation in space due to their attractive sizing, power, and cost properties when compared with conventional flight hardware or costly laser-based systems. However, a camera cannot infer depth information on its own, which is often solved by introducing complementary sensors or a second camera. In this paper, an innovative model-based approach is demonstrated to estimate the six-dimensional pose of a target relative to the chaser spacecraft using solely a monocular setup. The observed facet of the target is tackled as a classification problem, where the three-dimensional shape is learned offline using Gaussian mixture modeling. The estimate is refined by minimizing two different robust loss functions based on local feature correspondences. The resulting pseudomeasurements are processed and fused with an extended Kalman filter. The entire optimization framework is designed to operate directly on the SE(3) manifold, uncoupling the process and measurement models from the global attitude state representation. It is validated on realistic synthetic and laboratory datasets of a rendezvous trajectory with the complex spacecraft Envisat, demonstrating estimation of the relative pose with high accuracy over full tumbling motion. Further evaluation is performed on the open-source SPEED dataset

SoftPOSIT enhancements for monocular camera spacecraft pose estimation

This paper proposes several enhancements to the softPOSIT algorithm with applications to spacecraft pose estimation using a monocular camera. First, the proposed enhancements include a technique for reducing false matches as result of local minimum trapping. Second, this paper provides two strategies for iteration control parameter initialization by using the trace of the correspondence distance, and by using image centroid matching. The method of image centroid matching allows the world model center of geometry to align with the image centroid. The alignment result in reasonable correspondence weighting values used for match optimization. The various algorithm enhancements were tested on 26,180 simulations with varying geometries and initial pose conditions. Results show a significant increase in accuracy when compared with the original method