Probabilistic localization approaches like Kalman or Monte Carlo benefit from an accurate prediction step. The more accurate the prediction step, the more accurate is the belief of the robots pose. In most approaches probabilistic motion models are applied, mainly because robot dynamics are more difficult to model. Still some approaches rely on dynamic models in order to increase the accuracy.
Therefore, I was wondering if it’s reasonable to utilize a robotic simulator like V-REP or Gazebo for the prediction step. The advantages I see in doing so are the following:
- the robots kinematic is solved by default, simply through modeling it in the robotic simulator
- the robots dynamics are taken into account
- nonlinear behaviors like slippage or collision can be modelled up to a certain extend
- the robots workspace is taken into account, by modeling its environment (if the robot drives against a wall previous models would predict it behind the wall, which won’t happen in a robotic simulator)
With the shown advantages I hope to achieve a more accurate prediction.
However there might be some problems using a robotic simulator. For a start it has to ensure real time behavior and there will be delay in the prediction due to the communication with the simulator.
I was looking for some papers which pick up on that idea but couldn’t find any. Are there any approaches similar to my idea? If not, are there any reasons why nobody is using a robotic simulator for the prediction? What are your opinions about my proposal?