Path efficient level set estimation for mobile sensors

Lorenzo Bottarelli, Jason Blum, Manuele Bicego, Alessandro Farinelli

Abstract: The interest in using robotic sensors for monitoring spatial phenomena is steadily increasing. In the context of environmental analysis, operators typically focus their attention where measurements belong to a region of interest (e.g., when monitoring a body of water we might want to determine where the pH level is above a critical threshold). Most of the previous work in the literature represents the environmental phenomena with a Gaussian Process model, and then uses such a model to determine the best locations for measurements [3, 7]. In this paper we consider a specific scenario where a mobile platform with low computational power can continuously acquire measurements with a negligible cost. In this scenario, we seek to reduce the distance traveled by the mobile platform as it gathers information and to reduce the computation required by this path selection process. Starting from the LSE algorithm [7], we propose two novel approaches, PULSE and PULSE-batch, that exploit a new fast path selection procedure. We evaluate the effectiveness of our approaches on two datasets: a dataset of the pH level of the water, acquired with a mobile watercraft, and a publicly available dataset that represents CO2 maps. Results show that our techniques can compute informative paths with a computation time that is an order of magnitude lower than other techniques.