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 certain types of environments, while others are more efficient in others. This paper describes a data fusion method, where the differences between measurements are used to identify the type of terrain through which the vehicle is traveling. In this system, position estimate by odometry is compared to that calculated by triangulation, and the differences are fed into a neural network. This neural network, which is pertained by a set of different terrain types, classifies the examined environment by matching it with the most similar environment it can "recognize".