A stochastic environment modelling method for mobile robot by using 2-D laser scanner

Abstract

A method of environment modelling is presented which is based on the stochastic approximation technique. The environment is represented in this paper as stochastic obstacle regions equipped with their own stochastic variables such as mean, variance and eigenvalues. The stochastic variables in each obstacle region are updated by using the distance information of the obstacles, which is acquired from the laser scanner sampling time. The representation of the environment with the stochastic variables enables us to save CPU time and memory consumption in building the map. This technique can also detect if the obstacles are removed and is applicable to the quasi-static environment. If the eigenvalues of the covariance matrix in a certain region are known, then the feature of that region can be extracted as a line or as an ellipse. Thus, the algorithm presented can be used for the navigation and the localization of the mobile robot. The algorithm presented is successfully tested on our mobile robot ARES-II system equipped with the LADAR 2D-laser scanner.