On-board vision-based spacecraft estimation algorithm for small body exploration

Abstract

A methodology is summarized for designing on-board state estimators in support of spacecraft exploration of small bodies such as asteroids and comets. This paper focuses on an estimation algorithm that incorporates two basic computer-vision measurement types: a landmark table (LMT) and a paired feature table (PFT). Several innovations are developed to incorporate these measurement types into the on-board state estimation algorithm. Simulations are provided to demonstrate the feasibility of the approach.

Constrained estimation for autonomous navigation using unknown landmarks

“…Such method is called implicit bias method by delay state augmentation [4], it is referred to as "stochastic cloning" in the machine vision literature [13]. For the sake of completeness, implicit bias method will be developed in this section.…”

“…One is an implicit bias method without estimating landmark's position. Such method combines two or more observations at different times into a single one that does not depend upon the landmark's position [2], [3] and spacecraft's orbital information is estimated using a kalman filter by delay-state augmentation [4]. Another approach is augmenting landmark's position with the spacecraft's position and velocity vector to form a new state vector and then a Kalman filter is used to estimate spacecraft's state as well as the landmark's position [5].…”

A sparsity-aware QR decomposition algorithm for efficient cooperative localization

“…is the condition number of H], which renders the algorithm (8)- (9) numerically less robust [21], [22].…”

“…An effective strategy to overcome the numerical instability of the Information filter [see (8)- (9)] is to apply thin QR factorization [22] on H k [21], i.e.,…”

“…3) Computing Γ (ℓ) : To determine Γ (ℓ) , we begin with (21) and (17). Recall that we do not have the explicit expression of H corresponds to…”