Informative Path Planning based on Global and Local Active Sensing for Environmental Field Mapping

Mobile robots can reconstruct environmental fields through active sensing and autonomous navigation, so as to achieve online environmental monitoring. Active sensing enables determine the target sampling location by exploiting the critical information in the target monitored environments. The key information for active sensing is commonly determined by the objective function based on environmental field statistics. In this paper, a novel global and local informative path planning method based on Gaussian process (GP) and multivariate conditional mutual information (MCMI) is proposed, which can achieve effective and reliable execution of information-based active sensing and path planning. Experimental results demonstrate the performance of the proposed method for robotic mobile sensing and environmental field mapping.