How do I use Kalman Filter for data with error accumulation
I use UKF for robot localization (via FilterPy). I understand how to do implementation in general, and GPS sensor already works.
My question is about adding odometry from dif. drive. Odometry errors accumulate, so if I provide the filter with (x,y,heading) from ROS2 dif. drive odometry, it will become more and more garbage. I thought that if I use velocity/angular velocity instead, I’ll be fine because those do not accumulate error. But what I see is improvement, compared to GPS-only case, at the beginning, but then it looks identical to GPS-only.