  Inertial-sensor-based attitude estimation is a crucial technology in various
applications, from human motion tracking to autonomous aerial and ground
vehicles. Application scenarios differ in characteristics of the performed
motion, presence of disturbances, and environmental conditions. Since
state-of-the-art attitude estimators do not generalize well over these
characteristics, their parameters must be tuned for the individual motion
characteristics and circumstances. We propose RIANN, a real-time-capable neural
network for robust IMU-based attitude estimation, which generalizes well across
different motion dynamics, environments, and sampling rates, without the need
for application-specific adaptations. We exploit two publicly available
datasets for the method development and the training, and we add four
completely different datasets for evaluation of the trained neural network in
three different test scenarios with varying practical relevance. Results show
that RIANN performs at least as well as state-of-the-art attitude estimation
filters and outperforms them in several cases, even if the filter is tuned on
the very same test dataset itself while RIANN has never seen data from that
dataset, from the specific application, the same sensor hardware, or the same
sampling frequency before. RIANN is expected to enable plug-and-play solutions
in numerous applications, especially when accuracy is crucial but no
ground-truth data is available for tuning or when motion and disturbance
characteristics are uncertain. We made RIANN publicly available.
