Data Filtering Systems

The purpose of the Data Filtering System is to take in noisy data from the various sensors (via the Data Acquisition System) and to form an accurate and cohesive picture of the robot's surroundings. It will then pass that picture to the Path Planning System.

Filters

Kalman Filter

The Kalman filter is a relatively simple method for determining parameters of a system from noisy measurements of that system. It will be a good start for future studies in more complicated methods. It first assumes that all of the noise in the system is Gaussian and then recursively estimates the unknown variables. To implement this we will need to make measurement models for each of the sensors and the model for the motors. This will involve a lot of discussion with the Environment Data Processing System to negotiate what kind of data will be sent from the camera and LIDAR.

Models

Bayesian filtering, and probabilistic estimation in general, uses models of the system being studied to approximate unknown variables when given other information (measurements and controls) about the system. For robotics, we usually split these models up into world/control models and measurement models. In SLAM, internal world models are usually separate for external world models.

World/Control Models

World models estimate how the system changes between states when no controls are present. Control models estimate how control actions on the system will affect the state. We usually assume that control actions on the system increase the uncertainty.

Measurement Models

We usually assume that control actions on the system decrease the uncertainty.