In-Air Imaging Sonar Sensor Network with Real-Time Processing Using GPUs

TL;DR

This paper introduces a flexible sensor network framework for in-air imaging sonar, utilizing GPU acceleration to enable real-time, multi-sensor data processing for autonomous navigation in complex environments.

Contribution

It presents a novel sensor network framework and GPU-based processing algorithm to achieve real-time, multi-sensor imaging sonar data processing.

Findings

GPU implementation reduces processing time

Enables real-time multi-sensor data fusion

Improves environmental sensing in challenging conditions

Abstract

For autonomous navigation and robotic applications, sensing the environment correctly is crucial. Many sensing modalities for this purpose exist. In recent years, one such modality that is being used is in-air imaging sonar. It is ideal in complex environments with rough conditions such as dust or fog. However, like with most sensing modalities, to sense the full environment around the mobile platform, multiple such sensors are needed to capture the full 360-degree range. Currently the processing algorithms used to create this data are insufficient to do so for multiple sensors at a reasonably fast update rate. Furthermore, a flexible and robust framework is needed to easily implement multiple imaging sonar sensors into any setup and serve multiple application types for the data. In this paper we present a sensor network framework designed for this novel sensing modality. Furthermore, an implementation of the processing algorithm on a Graphics Processing Unit is proposed to potentially decrease the computing time to allow for real-time processing of one or more imaging sonar sensors at a sufficiently high update rate.