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Global localization for mobile robots using reference scan matching

  • Robotics and Automation
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Abstract

This paper presents a new approach based on scan matching for global localization with a metric-topological hybrid world model. The proposed method aims to estimate relative pose to the most likely reference site by matching an input scan with reference scans, in which topological nodes are used as reference sites for pose hypotheses. In order to perform scan matching we apply the spectral scan matching (SSM) method that utilizes pairwise geometric relationships (PGR) formed by fully interconnected scan points. The SSM method allows the robot to achieve scan matching without using an initial alignment between two scans and geometric features such as corners, curves, or lines. The localization process is composed of two stages: coarse localization and fine localization. Coarse localization with 2D geometric histogram constructed from the PGR is fast, but not precise sufficiently. On the other hand, fine localization using the SSM method is comparatively slow, but more accurate. This coarse-to-fine framework reduces the computational cost, and makes the localization process reliable. The feasibility of the proposed methods is demonstrated by results of simulations and experiments.

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Author information

Authors and Affiliations

  1. Global Technology Center, Samsung Electronics, Maetan 3-dong, Yeongtong-gu, Suwon-si, Gyeonggi-do, 443-803, Korea

    Soonyong Park

  2. Center for Bionics/Biomedical Research Institute, Korea Institute of Science and Technology (KIST), Hwarangno 14-gil 5, Seongbuk-gu, Seoul, 136-791, Korea

    Sung-Kee Park

Authors
  1. Soonyong Park
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  2. Sung-Kee Park
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Corresponding author

Correspondence to Sung-Kee Park.

Additional information

Recommended by Associate Editor Seul Jung under the direction of Editor Hyouk Ryeol Choi.

This work was supported by the KIST Institutional Program (2E24123) and the NAP (National Agenda Project) of the Korea Research Council of Fundamental Science & Technology.

Soonyong Park received his B.S. and M.S degrees in Mechanical Engineering from Kyunghee University, Seoul, Korea, in 2001 and 2003, respectively. He received his Ph.D. degree in Electrical and Electronic Engineering from Yonsei University, Seoul, Korea, in 2010. From 2001 to 2010, he was a graduate student researcher and a postdoctoral fellow with the Cognitive Robotics Center, Korea Institute of Science and Technology (KIST), Seoul, Korea. He was a postdoctoral fellow with the Robotics Institute, Carnegie Mellon University (CMU), in 2011. From 2012 to 2013, he was a research staff member in the Samsung Advanced Institute of Technology (SAIT) at Samsung Electronics. He is now a senior engineer in the Global Technology Center at Samsung Electronics. His research interests include computer vision, machine learning, and mobile robot navigation.

Sung-Kee Park is a principal research scientist for Korea Institute of Science and Technology (KIST). He received his B.S. and M.S. degrees in Mechanical Design and Production Engineering from Seoul National University, Seoul, Korea, in 1987 and 1989, respectively. He received his Ph.D. degree from Korea Advanced Institute of Science and Technology (KAIST), Korea, in the area of computer vision in 2000. Since then, he has been studying the field of robot vision and cognitive robotics at KIST. During his period at KIST, he held a visiting position in the Robotics Institute at Carnegie Mellon University (CMU) in 2005, where he did research on object recognition. Now he is working for the center of Bionics at KIST. His recent work has been on object recognition, visual navigation, video surveillance, human-robot interaction, and socially assistive robot.

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Park, S., Park, SK. Global localization for mobile robots using reference scan matching. Int. J. Control Autom. Syst. 12, 156–168 (2014). https://doi.org/10.1007/s12555-012-9223-0

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  • DOI: https://doi.org/10.1007/s12555-012-9223-0

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