Position estimation for mobile robot using sensor fusion

An accurate position estimation is essential for a mobile robot, especially under partially known environment. Dead reckoning has been commonly used for position estimation. However this method has inherent problems because it also accumulate estimation errors. In this paper we propose two methods to increase the accuracy of estimated positions using multiple sensors information. One method is a probabilistic approach using Bayes rule, and the other is a matching method applying least squared scheme. Both of these two approaches use features, such as corner points and edges of the object in the task environment instead of land-marks. It is shown that we will be able to estimate the position of mobile robot precisely, in which errors are not cumulated.<>