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Kalman filters can get really complicated (EKF, UKF, adaptive, etc etc) and needs to be tuned with the correct covariances.

Complementary filters works well enough and is very simple to implement even if you're a noob (e.g., high pass on accelerometer and low pass on gyro).

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There's a lot of libraries that do the hard work for you though... Anyway, if an EKF is too much effort, you could also try the "Madgwick" filter:

Madgwick, S. O., Harrison, A. J., & Vaidyanathan, R. (2011). Estimation of imu and marg orientation using a gradient descent algorithm. In Proceedings of the IEEE International Conference on Rehabilitation Robotics

The Madgwick filter seems to be popular for gyroscope applications, this might be very helpful. Thanks!

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