The following is a simple reference implementation of Simultaneous Localization And Mapping written in MATLAB. The code simulates a robot cruising around in a 2D world which contains 5 uniquely identifiable landmarks at unknown locations. The robot is equipped with a sensor that can measure the range and angle to these landmarks, but each measurement is corrupted by noise.

This implementation is based on Thrun et. al's FastSLAM 1.0 algorithm, though all optimizations have been removed for clarity. The algorithm works by representing the position of the robot via a Particle Filter. Each particle contains 5 completely independent Extended Kalman Filters, each of which is responsible for tracking a single landmark. After each measurement of a landmark, each particle is given a score based on how closely the latest landmark measurement matched its expectation. The list of particles is then resampled such that particles with high scores are likely to be replicated, and those with low scores are likely to be deleted.

To run this demo, just checkout this project, and run slam.m

• My introductory lecture on Particle Filters
• My SimpleKalman project