Local environment recognition based on the data fusion of LMS and vision

In order to guide the UGV detection, a new method of local environment recognition based on the data fusion of LMS and vision were given and tested. Firstly the structure of LMS system and vision system has been modeled separately, and then the algorithm was showed to describe the data processing procedure. The grid method has been used to classify obstacle points and evaluate their cost. Considering the motor vehicle trafficability, an upgraded Dempster-Shafer criterion was used to revise the decision-making of driving. The trafficability was evaluated by reliability matrix. At last, the experiment has been done to validate the whole system.