Gaussian Process As a Benchmark for Optimal Sensor Placement Strategy

Nalika Ulapane, Karthick Thiyagarajan, Sarath Kodagoda

Abstract: Optimal sensor placement is an important problem to look at. This problem becomes all the more relevant nowadays due to advancements in infrastructure monitoring robotic technologies including underground sensing. While there are multiple ways to solve optimal sensor placement problems, one of the most generic methods available is Bayesian Optimization and its variants. In this paper, we present a simple benchmark-like formulation for exploiting Gaussian Process uncertainty for sensor placement to measure a scalar field.