  Unlike a traditional gyroscope, a visual gyroscope estimates camera rotation
through images. The integration of omnidirectional cameras, offering a larger
field of view compared to traditional RGB cameras, has proven to yield more
accurate and robust results. However, challenges arise in situations that lack
features, have substantial noise causing significant errors, and where certain
features in the images lack sufficient strength, leading to less precise
prediction results.
  Here, we address these challenges by introducing a novel visual gyroscope,
which combines an analytical method with a neural network approach to provide a
more efficient and accurate rotation estimation from spherical images. The
presented method relies on three key contributions: an adapted analytical
approach to compute the spherical moments coefficients, introduction of masks
for better global feature representation, and the use of a multilayer
perceptron to adaptively choose the best combination of masks and filters.
Experimental results demonstrate superior performance of the proposed approach
in terms of accuracy. The paper emphasizes the advantages of integrating
machine learning to optimize analytical solutions, discusses limitations, and
suggests directions for future research.
