Determining Position Using Monte Carlo Localization
We now have several interesting sensors on our robot. However, we have yet to combine them to understand the position of our robot. The Monte Carlo simulation is a method that uses multiple sensors and a model of a robot’s world to estimate its location and heading in that world.
You will learn how to make a test arena for a robot, followed by how to model this arena in code, and how to send this data over Bluetooth to view on a computer. You will practice statistical methods for the robot to start guessing its location. You will see how to enrich encoder data and move the guesses, and then integrate this with distance sensor data to refine the guesses, using a method that is effective in the face of noisy sensor data and can cope with minor inaccuracies. This will come together in a Monte Carlo guess and check loop.
In this chapter, we will cover the following main topics:
- Creating a training area for our robot...
