GAUSSIAN PROCESS SEQUENTIAL FILTERING FOR SMALL BODY SLAM WITH SILHOUETTE-BASED MEASUREMENTS

A Gaussian Process Sequential Filter (GPSF) is developed for Simultaneous Localization and Mapping (SLAM). The GPSF models the small body shape through spatially correlated basis nodes and radii, and si- multaneously estimates the body’s shape, orientation, and spin, along with the spacecraft’s relative position and ve- locity. The formulation of the GPSF is provided, includ- ing the root-solved analytic partials, measurement under- weighting techniques, and covariance inflation methods. The performance of the GPSF is highlighted with a Monte Carlo study about multiple small bodies, including Lutetia, Eros, Toutatis, and Bennu.