A study on the environmental map building for a mobile robot using infrared range-finder sensors

This paper presents a methodology for building the high accuracy environmental map using a mobile robot. The design approach uses low cost infrared range-finder sensors incorporating with neural networks. To enhance the map quality, the errors occurring from the sensors are corrected. The nonlinearity error of the sensors is compensated using a backpropagation neural network and the random error of readings including the uncertainty of environment is taken into a sensor model at probabilistic approach. The map is represented by occupancy grid framework and updated by the Bayesian estimation mechanism. The effectiveness of the proposed method is verified through a series of experiments.