Odometry and triangulation data fusion for mobile-Robots environment recognition

Autonomous vehicles usually use more than one positioning system to improve their position estimate. Some positioning systems are advantageous in cert ain types of environments, while others are more efficient in others. This paper describes a data fusion method, where the differences between measure ments are used to identify the type of terrain through which the vehicle is traveling. In this system, position estimate by odometry is compared to th at calculated by triangulation, and the differences are fed into a neural n etwork. This neural network, which is pertained by a set of different terra in types, classifies the examined environment by matching it with the most similar environment it can "recognize". (C) 1998 Elsevier Science Ltd. All rights reserved.