Implicit Extended Kalman Filter for Optical Terrain Relative Navigation Using Delayed Measurements

The exploration of celestial bodies such as the Moon, Mars, or even smaller ones such as comets and asteroids, is the next frontier of space exploration. One of the most interesting and attractive purposes from the scientific point of view in this field, is the capability for a spacecraft to land on such bodies. Monocular cameras are widely adopted to perform this task due to their low cost and system complexity. Nevertheless, image-based algorithms for motion estimation range across different scales of complexities and computational loads. In this paper, a method to perform relative (or local) terrain navigation using frame-to-frame features correspondences and altimeter measurements is presented. The proposed image-based approach relies on the implementation of the implicit extended Kalman filter, which works using nonlinear dynamic models and corrections from measurements that are implicit functions of the state variables. In particular, here, the epipolar constraint, which is a geometric relationship between the feature point position vectors and the camera translation vector, is employed as the implicit measurement fused with altimeter updates. In realistic applications, the image processing routines require a certain amount of time to be executed. For this reason, the presented navigation system entails a fast cycle using altimeter measurements and a slow cycle with image-based updates. Moreover, the intrinsic delay of the feature matching execution is taken into account using a modified extrapolation method

Deep Reinforcement Learning-based policy for autonomous imaging planning of small celestial bodies mapping

This paper deals with the problem of mapping unknown small celestial bodies while autonomously navigating in their proximity with an optical camera. A Deep Reinforcement Learning (DRL) based planning policy is here proposed to increase the surface mapping efficiency with a smart autonomous selection of the images acquisition epochs. Two techniques are compared, Neural Fitted Q (NFQ) and Deep Q Network (DQN), and the trained policies are tested against benchmark policies over a wide range of different possible scenarios. Then, the compatibility with an on-board application is successfully verified, investigating the policy performance against navigation uncertainties

Centralized Autonomous Relative Navigation of Multiple CubeSats around Didymos System

This paper presents an on-board, centralized autonomous navigation algorithm able to reconstruct the trajectories of a fleet of CubeSats relative to an asteroid binary system. The algorithm is executed on-board the main spacecraft, which takes relative measurements using a narrow optical camera and an additional relative measurement, being inter satellite ranging in this paper. The image processing algorithm detects and tracks the CubeSats, deriving line-of-sight measurements. A non-linear estimation filter is employed to reconstruct the trajectory. The algorithm is tested using high-fidelity trajectory simulator and synthetic images generation routines