A simultaneous localization and map building algorithm based on sequential Monte Carlo method

In this study, a statistical estimation algorithm is developed to solve the SLAM (simultaneous localization and map building) problem, by using a robot equipped with only simple and cheap sensors. During map building and simultaneous localization, the robot can sense its environment with infrared sensors and can decide the path to follow by using the developed SLAM algorithm. The most frequent problems in SLAM algorithms are sensors' noise and odometry errors. To solve this problem, sequential Monte Carlo (SMC) method which is a well known particle filter application is used and promising results were obtained for the SLAM problem.