Quantifying Uncertainties in Exploration using Stereophotoclinometry with Topography (QUEST)

Image-based surface reconstruction and characterization is crucial for missions to small celestial bodies, as it informs mission planning, navigation, and scientific analysis. However, current state-of-the-practice methods, such as stereophotoclinometry (SPC), rely heavily on human-in-the-loop verification and high-fidelity information. The proposed work, called Quantifying Uncertainties in Exploration using Stereophotoclinometry with Topography (QUEST), extends a novel framework, called Photoclinometry-from-Motion (PhoMo), that incorporates photoclinometry techniques into a keypoint-based structure-from-motion system. PhoMo estimates the surface normal and albedo at detected landmarks to improve surface and shape characterization of small celestial bodies from in-situ imagery. QUEST significantly advances the PhoMo framework by computing the uncertainties associated with the PhoMo-generated map and using these results to compute optimal terrain relative navigation (TRN) estimates. In particular, both analytical- and numerical-based covariance expressions will be derived using an Unscented Kalman Filter (UKF) to quantify the errors in the QUEST-derived TRN estimates. QUEST will compare the use of a UKF with the factor graph-based smoothing approach that already exists within the PhoMo framework. The UKF is expected to provide significant advantages to 1) assess the expected TRN performance, which can be used for typical error-budget assessments, and 2) increase the existing performance. Another aspect that will be studied is the computational effort required to obtain real-time TRN solutions. Results will be computed using data from NASA’s Planetary Data System.