A metric approach for simultaneous localization and map building

Simultaneous localization and mapping is a stochastic map building method which permits consistent robot navigation without requiring an a priori map. The map is built incrementally as the robot observes the environment with its on-board sensors and, at the same time, is used to localise the robot. In this paper simultaneous localization and map building is performed with a metric approach which permits both precision and robustness. The most important innovation of the approach is the way how errors in the control of robot localisation are handled by map building using the information of the landmarks localization. The method uses data from a laser scanner to extract distances and orientations of landmark and combines control localization and metric paradigm. The metric approach, based on the Kalman Filter, uses a new concept to avoid the problem of the drift in odometry. The simulation section will validate the maps representation approach and presents different aspect of environments.