Implementación de algoritmos filtro de Kalman y filtro de Partículas para localización y mapeo simultáneo aplicado a un robot móvil en ambientes interiores con variaciones de iluminación

Abstract

This project evaluates the parameters influencing light variations in the operation of the Kalman Filter and Particle Filter algorithms applied to SLAM (Simultaneous Mapping and Localization), carrying out experiments in closed environments that allow tests with different light intensities. For this, the Turtlebot3 Burger differential mobile robot is used accompanied by the Kinect V1 sensor, since it has a depth sensor and an RGB camera, this allows to carry out 3D map construction and show variations in the mapping processes depending on the lighting, which is a requirement of this job. The particle filter (PF) is one of the most adapted estimation algorithms for SLAM, as is the Kalman filter that interacts with past, present and future system estimates, being used to follow dynamic stochastic systems using noisy sensors. The algorithms use a prediction and correction method, that is, they forecast a new state from its previous estimate by adding a correction factor of the same proportion to the prediction error, in such a way that this error is minimized with each iteration, which which makes it suitable for working with lighting variations since this technique demands that any autonomous system can be rebuilt and located in the shortest possible time under natural conditions.