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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)
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