Modern navigation solutions are largely dependent on the performances of the
standalone inertial sensors, especially at times when no external sources are
available. During these outages, the inertial navigation solution is likely to
degrade over time due to instrumental noises sources, particularly when using
consumer low-cost inertial sensors. Conventionally, model-based estimation
algorithms are employed to reduce noise levels and enhance meaningful
information, thus improving the navigation solution directly. However,
guaranteeing their optimality often proves to be challenging as sensors
performance differ in manufacturing quality, process noise modeling, and
calibration precision. In the literature, most inertial denoising models are
model-based when recently several data-driven approaches were suggested
primarily for gyroscope measurements denoising. Data-driven approaches for
accelerometer denoising task are more challenging due to the unknown gravity
projection on the accelerometer axes. To fill this gap, we propose several
learning-based approaches and compare their performances with prominent
denoising algorithms, in terms of pure noise removal, followed by stationary
coarse alignment procedure. Based on the benchmarking results, obtained in
field experiments, we show that: (i) learning-based models perform better than
traditional signal processing filtering; (ii) non-parametric kNN algorithm
outperforms all state of the art deep learning models examined in this study;
(iii) denoising can be fruitful for pure inertial signal reconstruction, but
moreover for navigation-related tasks, as both errors are shown to be reduced
up to one order of magnitude.Comment: 10 pages, 15 figures, 8 table