Sensor Relevance Validation for Autonomous Mobile Robot Navigation

We have defined a new paradigm of robot navigation, by using sensors in the environment instead of onboard robotic platform. In this scenario, determining the output of the most relevant and validated sensor is of crucial importance when heterogeneous sensors are available for measuring a given process. We are using an IEEE 1451 TEDS (transducer electronic data sheets) compliant sensor model proposed earlier for heterogeneous sensor networks. The presented method computes at each time step the usefulness of sensor information from discovered sensors, based upon Kullback-Leibler divergence between the sources of information. The robotic application consequently can obtain the relevance of the sensor output and the robot can update its estimate of the environment. We present results of a real time implementation of heterogeneous sensor networks using distributed multi-sensing 3D real-time robotics software player/Gazebo on a simulated pioneer robot. The results show that the proposed model can be utilized in the real-time scenario and can help reduce the computational cost of a system which occurs due to lack of discriminatory information