  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.
