A study of 2D indoor localization and mapping using FastSLAM 2.0

FastSLAM 2.0 is a Simultaneous Localization And Mapping (SLAM) framework that provides an algorithm for calculating robot's pose and map the environment concurrently. It consists of calculation that include getting data from sensor, associate the data, and update the map. SLAM is a challenging problem in Robotics because it is considered a chicken-and-egg problem. In this paper, ite will be studied the FastSLAM 2.0 algorithm that will be using RANSAC (Random Sampling Consensus) for its feature extraction.