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Three-Dimensional Reconstruction Based on Visual SLAM of Mobile Robot in Search and Rescue Disaster Scenarios

Published online by Cambridge University Press:  21 May 2019

Hongling Wang ,
Chengjin Zhang ,
Yong Song ,
Bao Pang  and
Guangyuan Zhang
Hongling Wang
Affiliation:
School of Control Science and Engineering, Shandong University, Ji’nan 250101, China E-mail: pang_bao11@163.com School of Information Science and Electronic Engineering, Shandong Jiao Tong University, Ji’nan 250357, China. E-mails: wanghongling@sdjtu.edu.cn, zhangguangyuan@sdjtu.edu.cn
Chengjin Zhang*
Affiliation:
School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, Weihai 264209, China
Yong Song*
Affiliation:
School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, Weihai 264209, China
Bao Pang
Affiliation:
School of Control Science and Engineering, Shandong University, Ji’nan 250101, China E-mail: pang_bao11@163.com
Guangyuan Zhang
Affiliation:
School of Information Science and Electronic Engineering, Shandong Jiao Tong University, Ji’nan 250357, China. E-mails: wanghongling@sdjtu.edu.cn, zhangguangyuan@sdjtu.edu.cn
*
*Corresponding authors. E-mails: cjzhang@sdu.edu.cn, songyong@sdu.edu.cn
*Corresponding authors. E-mails: cjzhang@sdu.edu.cn, songyong@sdu.edu.cn
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Summary

Conventional simultaneous localization and mapping (SLAM) has concentrated on two-dimensional (2D) map building. To adapt it to urgent search and rescue (SAR) environments, it is necessary to combine the fast and simple global 2D SLAM and three-dimensional (3D) objects of interest (OOIs) local sub-maps. The main novelty of the present work is a method for 3D OOI reconstruction based on a 2D map, thereby retaining the fast performances of the latter. A theory is established that is adapted to a SAR environment, including the object identification, exploration area coverage (AC), and loop closure detection of revisited spots. Proposed for the first is image optical flow calculation with a 2D/3D fusion method and RGB-D (red, green, blue + depth) transformation based on Joblove–Greenberg mathematics and OpenCV processing. The mathematical theories of optical flow calculation and wavelet transformation are used for the first time to solve the robotic SAR SLAM problem. The present contributions indicate two aspects: (i) mobile robots depend on planar distance estimation to build 2D maps quickly and to provide SAR exploration AC; (ii) 3D OOIs are reconstructed using the proposed innovative methods of RGB-D iterative closest points (RGB-ICPs) and 2D/3D principle of wavelet transformation. Different mobile robots are used to conduct indoor and outdoor SAR SLAM. Both the SLAM and the SAR OOIs detection are implemented by simulations and ground-truth experiments, which provide strong evidence for the proposed 2D/3D reconstruction SAR SLAM approaches adapted to post-disaster environments.

Keywords

SLAM3D reconstructionRGB-D visual systemSAR environmentsRobot mapping

Information

Type
Articles
Information
Robotica , Volume 38 , Issue 2 , February 2020 , pp. 350 - 373
Copyright
© Cambridge University Press 2019 

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