I am attempting to teach myself sensor fusion as I suspect I'll need to do this down the road with lidar and some other sensors. In all my research so far it sounds like a version of the extended kalman-filter is the best way to do this.
I believe I have a good intuition for how the EKF works but I am stuck on a few things. First let me describe my understanding of the process in general.
- Starting at time $k_1$, use a motion model to predict the pose of the vehicle at time $k$
- Use a measurement model to describe where we can expect the measurement to occur
- Once a measurement is observed we use step 3 to decide how much of the measurement should be used to update the predicted state
- Update the predicted state with information from step 3.
- Repeat forever
What I am really stuck on is how do we measure the pose of the robot with a sensor like lidar or radar? Several examples I have found show the raw lidar data as if it were the measured pose of the robot, instead of the distance from a object. They use this raw data in step 4 above but this doesn't make sense to me. How can we get a better prediction of the state using the raw lidar data if we can't actually measure the state of the robot using the lidar?