Xu-Yang Shao, Guo-Hui Tian, Ying Zhang. A 2D Mapping Method Based on Virtual Laser Scans for Indoor Robots. International Journal of Automation and Computing, vol. 18, no. 5, pp.747-765, 2021. https://doi.org/10.1007/s11633-021-1304-1
Citation: Xu-Yang Shao, Guo-Hui Tian, Ying Zhang. A 2D Mapping Method Based on Virtual Laser Scans for Indoor Robots. International Journal of Automation and Computing, vol. 18, no. 5, pp.747-765, 2021. https://doi.org/10.1007/s11633-021-1304-1

A 2D Mapping Method Based on Virtual Laser Scans for Indoor Robots

doi: 10.1007/s11633-021-1304-1
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  • Author Bio:

    Xu-Yang Shao received the B. Sc. degree in automation from Qingdao University of Science and Technology, China in 2018. He is now a master student in control engineering, Shandong University, China.His research interests include SLAM, indoor mapping, autonomous navigation, and service robots. E-mail: 201834562@mail.sdu.edu.cn ORCID iD: 0000-0002-4723-3077

    Guo-Hui Tian received the B. Sc. degree in control science from Department of Mathematics, Shandong University, China in 1990, the M. Sc. degree in industrial automation from Department of Automation, Shandong University of Technology, China in 1993, and the Ph. D. degree in automatic control theory and application from School of Automation, Northeastern University, China in 1997. He studied as a post-doctorial researcher in School of Mechanical Engineering, Shandong University, China from 1999 to 2001, and studied as a visiting professor in Graduate School of Engineering, Tokyo University, Japan from 2003 to 2005. He was a lecturer from 1997 to 1998 and an associate professor from 1998 to 2002 in Shandong University, China. At present, he is a professor in School of Control Science and Engineering, Shandong University, China. And also he is the Vice Director of the Intelligence Robot Specialized Committee of Chinese Association for Artificial Intelligence, the Vice Director of the Intelligent Manufacturing System Specialized Committee of Chinese Association for Automation, and the member of the IEEE Robotics and Automation Society. His research interests include service robot, intelligent space, cloud robotics, and brain-inspired intelligent robotics. E-mail: g.h.tian@sdu.edu.cn (Corresponding author) ORCID iD: 0000-0001-8332-3064

    Ying Zhang received the B. Sc. degree in automatic control from Heze University, China in 2014, the M. Sc. degree in control theory and control engineering from Shandong Jianzhu University, China in 2017. He is a Ph. D. degree candidate in control theory and control engineering at Shandong University, China.His research interests include intelligent robot system, knowledge representation and mapping.E-mail: zhangying0612@mail.sdu.edu.cnORCID iD: 0000-0001-8982-8223

  • Received Date: 2020-10-22
  • Accepted Date: 2021-04-29
  • Available Online: 2021-09-08
  • Publish Date: 2021-10-01
  • The indoor robots are expected to complete metric navigation tasks safely and efficiently in complex environments, which is the essential prerequisite for accomplishing other high-level operation tasks. 2D occupancy grid maps are sufficient to support the robots in avoiding all obstacles in the environments during navigation. However, the maps based on normal laser scans only reflect a horizontal slice of the environment, which may cause the problem of some obstacles missing or misinterpreting their exact boundaries, thereby threatening the safety and efficiency of robot navigation. This paper presents a 2D mapping method based on virtual laser scans to provide a more comprehensive representation of obstacles for indoor robot navigation. The resulting maps can accurately represent the top-down projected contours of all obstacles no matter where their vertical positions are. The virtual laser scans are initially generated from raw data of an RGB-D camera based on the filtering, projection, and polar-coordinate scanning. The scans are fed directly to the laser-based simultaneous localization and mapping (SLAM) algorithms to update the current map and robot position. Two auxiliary strategies are proposed to further improve the quality of maps by reducing the impact of the narrow field of view and the blind zone of the RGB-D camera on the observations. In this paper, the improved virtual laser generation method makes the extracted 2D observations fit the laser-based SLAM algorithms, and two auxiliary strategies are novel ways to improve map quality. The generated maps can reflect the comprehensive obstacle information in indoor environments with good accuracy. The comparative experiments are carried out based on four simulation scenarios and three real-world scenarios to prove the effectiveness of our 2D mapping method.

     

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