Automatic Position Estimation of a Mobile Robot

In order to monitor a robot's position, traditional techniques have used a model of the environment and a 2D view (image) of the scene from the current position of the robot. These techniques require either a fairly accurate 3D scene model or noisy 3D models with a reliable estimate of the model noise. Since it is dii-cult either to automatically obtain accurate 3D models or to reliably estimate the model noise, the 3D models are constructed manually. In this paper we propose automating the model generation step by using a robust model acquisition algorithm which also provides an estimate of the error in the acquired model. This paper shows that the acquired 3D model along with the error estimate is reliable enough for a position estimation algorithm.