OAS-GPUCB: On-the-way Adaptive Sampling Using GPUCB for Bathymetry Mapping

Rajat Agrawal, Karthik Nambiar, Bhawana Chhaglani, P.B. Sujit and Mandar Chitre

Abstract

Bathymetry mapping of static water bodies like lakes is essential for sustainable ecosystem development strate- gies. However, bathymetry mapping using (i) traditional manual sampling approaches using single-beam echosounder (SBE) has large mapping errors and (ii) performing multi-beam survey is very expensive. Alternatively, performing lawn-mower SBE sur- veys is time consuming due to limited field-of-view. In order to address the above issues, in this paper, we present an on-the-way sampling approach with Gaussian Process Upper Confidence Bound (GPUCB) algorithm called as OAS-GPUCB that can adaptively sample the lake to minimize the bathymetry error while reducing the distance travelled to achieve a given mapping accuracy. We validate the proposed approach using simulations on actual lake bathymetry maps and also carry out real- world experiments using an Autonomous Surface Vehicle(ASV) with SBE. Further, we compare OAS-GPUCB to lawn-mower, GPUCB, and GPUCB with fixed radius approaches. The results consistently show that the proposed approach can achieve less than 10% bathymetry error while achieving distance reduction of more than 55%compared to the lawn-mower approach, and more than 90% less distance travelled compared to GPUCB and GPUCB with fixed radius approaches. The results shows the general applicability of OAS-GPUCB for bathymetry mapping of water bodies without any prior information maps.