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.