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Yu, Hyeonwoo
Lab. of AI and Robotics
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dc.citation.conferencePlace US -
dc.citation.conferencePlace SOUTH KOREA -
dc.citation.endPage 1080 -
dc.citation.startPage 1077 -
dc.citation.title International Conference on Control, Automation and Systems -
dc.contributor.author Yu, Hyeonwoo -
dc.contributor.author Lee, Beomhee -
dc.date.accessioned 2024-02-01T01:10:25Z -
dc.date.available 2024-02-01T01:10:25Z -
dc.date.created 2022-02-07 -
dc.date.issued 2018-10-17 -
dc.description.abstract This paper presents a method to infer the terrain field and robot position exploiting the vibration obtained from the interaction between terrain and mobile robot. In order for robot localization and mapping in unknown area, simultaneous localization and mapping (SLAM) technique is applied to map the surrounding area. In this case, when SLAM is performed using a field such as WiFi, magnetic signal or terrain, a complete field map should be inferred for the unexplored region as well as the explored one. Also the uncertainty should be indicated for the inferred field, so that the field map can be used as observation model for SLAM. Therefore, by modeling the observation model for the terrain field with the Gaussian process, we estimate the observation probability distribution for the unknown regions. The inferred observation distribution can be used not only by field maps, but also by efficient path planning. We demonstrate the proposed method with the odometry of mobile robot navigating the testbed, and observations of terrain feature using simulation. -
dc.identifier.bibliographicCitation International Conference on Control, Automation and Systems, pp.1077 - 1080 -
dc.identifier.issn 2093-7121 -
dc.identifier.scopusid 2-s2.0-85060472074 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/80765 -
dc.identifier.wosid 000457612300165 -
dc.language 영어 -
dc.publisher IEEE -
dc.title Terrain field SLAM and Uncertainty Mapping using Gaussian Process -
dc.type Conference Paper -
dc.date.conferenceDate 2018-10-17 -

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