Do you know any papers on or implementations of GPS + IMU sensor fusion for localization that are not based on an EKF (Extended Kalman Filter) or UKF (Unscented Kalman Filter)?
I'm asking is because
- I've found KFs difficult to implement
- I want something simpler (less computationally expensive)
- rlabbe's book on Kalman Filters suggests they aren't ideal for this use case. In this chapter it says:
Kalman filters for inertial systems are very difficult, but fusing data from two or more sensors providing measurements of the same state variable (such as position) is quite easy.
Further in the "Can you Filter GPS outputs?" part
Hence, the signal is not white, it is not time independent, and if you pass that data into a Kalman filter you have violated the mathematical requirements of the filter. So, the answer is no, you cannot get better estimates by running a KF on the output of a commercial GPS. [...] This is a difficult problem that hobbyists face when trying to integrate GPS, IMU's and other off the shelf sensors.
If the above is true, other approaches should be out there. Yet answers to gps-imu fusion and implementation questions only point to EKFs. So far, I've only found a Master's thesis implementing an EIF (Extended Information Filter) and a paper with an Adaptive filter I don't quite understand. Do you know of alternative approaches?