A Critical Comparison on Attitude Estimation: From Gaussian Approximate Filters to Coordinate-free Dual Optimal Control

TL;DR

This paper critically compares Gaussian approximate filters and set-membership approaches for attitude and rate estimation without rate sensors, proposing a coordinate-free optimal filter based on control theory and validating through extensive simulations.

Contribution

It introduces a set-theoretic, coordinate-free optimal filter for attitude estimation, addressing limitations of Bayesian methods and deriving a novel predictive filter on $ ext{TSO}(3)$.

Findings

Set-theoretic approach overcomes Bayesian deficiencies

Coordinate-free filters improve robustness in attitude estimation

Validated effectiveness through extensive simulations

Abstract

This paper conveys attitude and rate estimation without rate sensors by performing a critical comparison, validated by extensive simulations. The two dominant approaches to facilitate attitude estimation are based on stochastic and set-membership reasoning. The first one mostly utilizes the commonly known Gaussian-approximate filters, namely the EKF and UKF. Although more conservative, the latter seems to be more promising as it considers the inherent geometric characteristics of the underline compact state space and accounts -- from first principles -- for large model errors. We address the set-theoretic approach from a control point of view, and we show that it can overcome reported deficiencies of the Bayesian architectures related to this problem, leading to coordinate-free optimal filters. Lastly, as an example, we derive a modified predictive filter on the tangent bundle of the special orthogonal group $\mathbb{TSO}(3)$.