I have a robot that has a GPS and velocity sensors. The GPS updates roughly every 1-2 seconds. I've been playing around with a Kalman filter that has been working pretty well. I just learned and finally think I understand particle filters so I'm wondering if a particle filter be useful to keep track of the robot's location in between GPS updates instead of the Kalman filter.
My plan would look something like this:
- Starting GPS coordinates.
- Create N random particles distributed around the starting coordinate (2 meters is the typical accuracy of most GPS sensors)
- Robot moves and records velocity data from the sensors
- Move all of the particles based on a linear model,velocity data, and noise
- With the next GPS sensor update weight the particles based on a Gaussian from the updated coordinates.
Am I on the right track or is this worth trying to code or should I just stick with the Kalman filter since it's a linear system?