Probabilistic Robotics

The world is complex and imperfect. For instance, surrounding environment can have disturbance and sensor data can have noise. Randomness is all over the place and the system is not deterministic.

To deal with randomness, we need a framework. In this chapter, we provide a brief description of the probabilistic robotics framework. The main objective is to estimate the state of the robot while dealing with the noise.

We start with Bayes Filter and introduce the basic Bayes Filter algorithm, Kalman filter and some non-parametric filters such as particle filter.

TODO