Towards Real-Time Interpolation for Enhanced AUV Deep Sea Mapping

TL;DR

This paper proposes a real-time interpolation architecture using GPU-accelerated algorithms for autonomous underwater vehicles to improve deep sea mapping efficiency and feasibility.

Contribution

It introduces an edge computing architecture that enables GPU-based interpolation algorithms to be run on AUVs for enhanced deep ocean exploration.

Findings

GPU interpolation algorithms are feasible on low-level GPUs onboard AUVs.

Edge computing reduces data transmission delays in deep sea mapping.

GPU acceleration improves interpolation speed and accuracy.

Abstract

Approximately seventy-one percent of the Earth is covered in water. Of that area, ninety-five percent of the ocean has never been explored or mapped. There are several engineering challenges that have prevented the exploration of the deep ocean through human or autonomous means. These challenges include but are not limited to high pressure, cold temperatures, little natural light, corrosion of materials, and communication. Ongoing research has been focused on trying to find optimal and low-cost solutions to effective communication between autonomous underwater vehicles (AUVs), and the surface or air. In this paper, an architecture is introduced that utilizes an edge computing approach to establish computation nearer to the source of data, allowing further exploration of the deep ocean. Taking the most common interpolation techniques used today in the field of bathymetry, the data are tested and analyzed to find the feasibility of switching from CPU to GPU computation. Specifically, the focus is on writing efficient interpolation algorithms that can be run on low-level GPUs, which can be carried onboard AUVs as payload.