Synthesis of indoor maps in presence of uncertainty

Abstract

A robotic system is presented, which is able to autonomously explore unengineered indoor environments, thereby synthesising maps suitable for planning and navigation purposes. Map recovery takes place through interaction between the robot and the world, in which either sensing and acting are affected by uncertainty. Kalman filtering is applied to maintain position best estimates, which are then fused with data coming from the observation of landmarks. The proposed method has been implemented on a robot equipped with ultrasonic range finders, and tested in a fairly simple, real environment.