Publications / 2018 Proceedings of the 35th ISARC, Berlin, Germany

Building an Integrated Mobile Robotic System for Real-Time Applications in Construction

Khashayar Asadi, Hariharan Ramshankar, Harish Pullagurla, Aishwarya Bhandare, Suraj Shanbhag, Pooja Mehta, Spondon Kundu, Kevin Han, Edgar Lobaton and Tianfu Wu
Pages 453-461 (2018 Proceedings of the 35th ISARC, Berlin, Germany)
Abstract:

One of the major challenges of a real-time autonomous robotic system for construction monitoring is to simultaneously localize, map, and navigate over the lifetime of the robot, with little or no human intervention. Past research on Simultaneous Localization and Mapping (SLAM) and context-awareness are two active research areas in the computer vision and robotics communities. The studies that integrate both in real-time into a single modular framework for construction monitoring still need further investigation. A monocular vision system and real-time scene understanding are computationally heavy and the major state-of-the-art algorithms are tested on high-end desktops and/or servers with a high CPU- and/or GPU- computing capabilities, which affect their mobility and deployment for real-world applications. To address these challenges and achieve automation, this paper proposes an integrated robotic computer vision system, which generates a real-world spatial map of the obstacles and traversable space present in the environment in near real-time. This is done by integrating contextual Aware- ness and visual SLAM into a ground robotics agent. This paper presents the hardware utilization and performance of the aforementioned system for three different outdoor environments, which represent the applicability of this pipeline to diverse outdoor scenes in near real-time. The entire system is also self-contained and does not require user input, which demonstrates the potential of this computer vision system for autonomous navigation.

Keywords: SLAM, Context awareness, Real-time integrated system, Robotic computer vision system, Construction monitoring