ComNSense

Abstract

Recently, point clouds have been efficiently utilized for medical imaging, modeling urban environments, and indoor modeling. In this realm, several mobile platforms, such as Google Tango and Apple ARKit, have been released leveraging 3D mapping, augmented reality, etc. In modeling applications, these modern mobile devices opened the door for crowd-sourcing point clouds to distribute the overhead of data collection. However, uploading these large points clouds from resources-constrained mobile devices to the back-end servers consumes excessive energy. Accordingly, participation rates in such crowd-sensing systems can be negatively influenced. To tackle this challenge, this paper introduces our ComNSense approach that dramatically reduces the energy consumption of processing and uploading point clouds. To this end, ComNSense reports only a set of extracted geometrical data to the servers. To optimize the geometry extraction, ComNSense leverages formal grammars which encode design-time knowledge, i.e. structural information. To demonstrate the effectiveness of ComNSense, we performed several experiments of collecting point clouds from two different buildings to extract the walls location, as a case study. We also assess the performance of ComNSense relative to a grammar-free method. The results showed a significant reduction of the energy consumption while achieving a comparable detection accuracy.