CNN based sensor fusion method for real-time autonomous robotics systems

Abstract

Autonomous robotic systems (ARS) serve in many areas of daily life. The sensors have critical importance for these systems. The sensor data obtained from the environment should be as accurate and reliable as possible and correctly interpreted by the autonomous robot. Since sensors have advantages and disadvantages over each other they should be used together to reduce errors. In this study, Convolutional Neural Network (CNN) based sensor fusion was applied to ARS to contribute the autonomous driving. In a real-time application, a camera and LIDAR sensor were tested with these networks. The novelty of this work is that the uniquely collected data set was trained in a new CNN network and sensor fusion was performed between CNN layers. The results showed that CNN based sensor fusion process was more effective than the individual usage of the sensors on the ARS.