A simple and efficient implementation of EKF - based SLAM relying on laser scanner in complex indoor environment

Localization in an unknown environment is one of the major issues faced by autonomous vehicles. The solution to this problem is delivered by the Simultaneous Localization and Mapping techniques, commonly known as SLAM. SLAM is the category of algorithms allowing a robot to map the surroundings and to keep an estimate of its position. Nowadays several SLAM methods are widely used. Though, many issues arise when SLAM is applied in a complex and unstructured environment. This article details an implementation of SLAM using improved Extended Kalman Filter (EKF). The aim is to provide a simple but reliable SLAM technique. The work has been carried out on a robot Seekur Jr, the mapping has been realized with a laser scanner. The applied EKF model with its modifications is presented. The techniques used to observe the environment and to identify the landmarks are outlined. The robustness and consistency of introduced modifications were justified by experiments.