Sensor Data Fusion Using Rough Set for Mobile Robots System
Wang Haijun, Chen Yimin · 2006
A multi-sensor data fusion framework for mobile robots self-localization in unknown environments is proposed. A mobile robot need to process much sensory data to extract accurate information from the robots' surroundings. Rough set theory offers new approaches to acquiring a set of classification rules from a decision table and reasoning under uncertain circumstances. So based on the rough set theory, we build the multi-sensor data fusion system model and propose an improved attribute reduction algorithm, by utilizing the algorithm, the rules for object recognition and classification are achieved. Finally, an illustrative example demonstrates the framework's effectiveness and validity