로봇학회논문지 (The Journal of Korea Robotics Society)

  • 제16권2호
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  • Pages.122-129
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  • 2021
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  • 1975-6291(pISSN)
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  • 2287-3961(eISSN)
한국로봇학회 (Korea Robotics Society)
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도시환경 매핑 시 SLAM 불확실성 최소화를 위한 강화 학습 기반 경로 계획법

RL-based Path Planning for SLAM Uncertainty Minimization in Urban Mapping

  • Cho, Younghun (Dept. of Civil and Environmental Engineering, KAIST) ;
  • Kim, Ayoung (Dept. of Civil and Environmental Engineering, KAIST)
  • 투고 : 2021.02.26
  • 심사 : 2021.04.05
  • 발행 : 2021.05.31
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초록

For the Simultaneous Localization and Mapping (SLAM) problem, a different path results in different SLAM results. Usually, SLAM follows a trail of input data. Active SLAM, which determines where to sense for the next step, can suggest a better path for a better SLAM result during the data acquisition step. In this paper, we will use reinforcement learning to find where to perceive. By assigning entire target area coverage to a goal and uncertainty as a negative reward, the reinforcement learning network finds an optimal path to minimize trajectory uncertainty and maximize map coverage. However, most active SLAM researches are performed in indoor or aerial environments where robots can move in every direction. In the urban environment, vehicles only can move following road structure and traffic rules. Graph structure can efficiently express road environment, considering crossroads and streets as nodes and edges, respectively. In this paper, we propose a novel method to find optimal SLAM path using graph structure and reinforcement learning technique.

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과제정보

This research was supported by a grant (21TSRD-B151228-03) from Urban Declining Area Regenerative Capacity-Enhancing Technology Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government

참고문헌

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