Improved mobile robot based gas distribution mapping through propagated distance transform for structured indoor environment. (18th May 2020)

• Record Type: Journal Article
• Authors: Visvanathan, Retnam; Kamarudin, Kamarulzaman; Mamduh, Syed Muhammad; Toyoura, Masahiro; Ali Yeon, Ahmad Shakaff; Zakaria, Ammar; Kamarudin, Latifah Munirah; Mao, Xiaoyang; Abdul Shukor, Shazmin Aniza
• Is Part Of: Advanced robotics. Volume 34:Number 10(2020)
• Page Start: 637
• Page End: 647
• Publication Date: 2020-05-18
• Subjects: Mobile olfaction -- gas distribution mapping -- gas source localization -- distance transform -- structured indoor environment; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2020.1748900
• Languages: English
• ISSNs: 0169-1864

Abstract

ABSTRACT: Mobile robot carrying gas sensors have been widely used in mobile olfaction applications. One of the challenging tasks in this research field is Gas Distribution Mapping (GDM). GDM is a representation of how volatile organic compound is spatially dispersed within an environment. This paper addresses the effect of obstacles towards GDM for indoor environment. This work proposes a solution by improvising the Kernel DM + V technique using propagated distance transform (DT) as the weighing function. Since DT computations are CPU heavy, parallel computing, using Compute Unified Device Architecture (CUDA) available in Graphics Processing Unit (GPU), is used to accelerate the DT computation. The proposed solution is compared with the Kernel DM + V algorithm, presenting that the proposed method drastically improves the quality of GDM under various kernel sizes. The study is also further extended towards the effect of obstacles on gas source localization task. The outcome of this work proves that the proposed method shows better accuracy for GDM estimation and gas source localization if obstacle information is considered. GRAPHICAL ABSTRACT: