Vision-based pose estimation for space objects by Gaussian process regression

We address the problem of vision-based pose estimation for space objects, which is to estimate the relative pose of a target spacecraft using imaging sensors. We develop a novel monocular vision-based method by employing Gaussian process regression (GPR) to solve pose estimation for space objects. GPR is a powerful regression model for predicting continuous quantities, and can easily obtain and express uncertainty. Assuming that the regression function mapping from the image (or feature) of the target spacecraft to its pose follows a Gaussian process (GP) properly parameterized by a mean function and a covariance function, the predictive equations can be easily obtained by a maximum-likelihood approach when training data are given. The mean value of the predicted output (i.e. the estimated pose) and its variance (which indicates the uncertainty) can be computed via these explicit formulations. Besides, we also introduce a manifold constraint to the output of GPR model to improve its performance for spacecraft pose estimation. We performed extensive experiments on a simulated image dataset that contains satellite images of 1D and 2D pose variation, as well as images with noises and different lighting conditions. Experimental results validate the effectiveness and robustness of our approach. Our model can not only estimate the pose angles of space objects but also provide the uncertainty of the estimated values which may be used to choose convincing results in applications.