This paper contains a concise comparison of a number of nonlinear attitude
filtering methods that have attracted attention in the robotics and aviation
literature. With the help of previously published surveys and comparison
studies, the vast literature on the subject is narrowed down to a small pool of
competitive attitude filters. Amongst these filters is a second-order optimal
minimum-energy filter recently proposed by the authors. Easily comparable
discretized unit quaternion implementations of the selected filters are
provided. We conduct a simulation study and compare the transient behaviour and
asymptotic convergence of these filters in two scenarios with different
initialization and measurement errors inspired by applications in unmanned
aerial robotics and space flight. The second-order optimal minimum-energy
filter is shown to have the best performance of all filters, including the
industry standard multiplicative extended Kalman filter (MEKF)
