Intrinsic filtering on Lie groups with applications to attitude estimation
This work addresses filtering challenges in attitude estimation for engineering applications, but it is incremental as it builds on existing invariant observer theory.
The paper tackles the problem of intrinsic filtering on matrix Lie groups with invariance properties by proposing a probabilistic approach, showing that the error equation is a Markov chain and proving convergence to a stationary distribution for noisy errors. It introduces the discrete-time Invariant Extended Kalman Filter and applies it to attitude estimation, deriving novel theoretical results and illustrating them through simulations.
This paper proposes a probabilistic approach to the problem of intrinsic filtering of a system on a matrix Lie group with invariance properties. The problem of an invariant continuous-time model with discrete-time measurements is cast into a rigorous stochastic and geometric framework. Building upon the theory of continuous-time invariant observers, we show that, as in the linear case, the error equation is a Markov chain that does not depend on the state estimate. Thus, when the filter's gains are held fixed, and the filter admits almost-global convergence properties with noise turned off, the noisy error's distribution is proved to converge to a stationary distribution, providing insight into the mathematical theory of filtering on Lie groups. For engineering purposes we also introduce the discrete-time Invariant Extended Kalman Filter, for which the trusted covariance matrix is shown to asymptotically converge, and some numerically more involved sample-based methods as well to compute the Kalman gains. The methods are applied to attitude estimation, allowing to derive novel theoretical results in this field, and illustrated through simulations on synthetic data.