ABSTRACT Adapting Monte Carlo Localization to Utilize Floor and Wall Texture Data

Monte Carlo Localization (MCL) is an algorithm that allows a robot to determine its location when provided a map of its surroundings. Particles, consisting of a lo-cation and an orientation, represent possible positions where the robot could be on the map. The probability of the robot being at each particle is calculated based on sensor input. Traditionally, MCL only utilizes the position of objects for localization. This the-sis explores using wall and floor surface textures to help the algorithm determine locations more accurately. Wall textures are captured by using a laser range finder to detect patterns in the surface. Floor textures are determined by using an inertial measurement unit (IMU) to capture acceleration vectors which represent the rough-ness of the floor. Captured texture data is classified by an artificial neural network and used in probability calculations. The best variations of Texture MCL improved accuracy by 19.1 % and 25.1 % when all particles and the top fifty particles respectively were used to calculate the robot’s estimated position. All implementations achieved comparable performance speed