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The Implementation of Graph-based SLAM Using General Graph Optimization

일반 그래프 최적화를 활용한 그래프 기반 SLAM 구현

  • 고낙용 (조선대학교 전자공학부) ;
  • 정준혁 (조선대학교 대학원 제어계측공학과) ;
  • 정다빈 (조선대학교 대학원 전자공학과)
  • Received : 2019.06.05
  • Accepted : 2019.08.15
  • Published : 2019.08.31

Abstract

This paper describes an implementation of a graph-based simultaneous localization and mapping(SLAM) method called the General Graph Optimization. The General Graph Optimization formulates the SLAM problem using nodes and edges. The nodes represent the location and attitude of a robot in time sequence, and the edge between the nodes depict the constraint between the nodes. The constraints are imposed by sensor measurements. The General Graph Optimization solves the problem by optimizing the performance index determined by the constraints. The implementation is verified using the measurement data sets which are open for test of various SLAM methods.

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그림 1. 노드와 엣지 Fig. 1 Nodes and edges

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그림 2. 일반 그래프 최적화 Viewer Fig. 2 General graph optimization viewer

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그림 3. 일반 그래프 최적화 데이터 형태 Fig. 3 일반 그래프 최적화 data format

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그림 4. ‘MIT-CSAIL’ 일반 그래프 최적화 및 매트랩 구동 결과 Fig. 4 'MIT-CSAIL' General graph optimization and matlab implementation results

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그림 5. ‘Intel Research Lab’ 일반 그래프 최적화 및 매트랩 구동 결과 Fig. 5 'Intel Research Lab' General graph optimization and matlab implementation results

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그림 6. ‘Wood autumn’ 일반 그래프 최적화 및 매트랩 구동 결과 Fig. 6 ‘Wood autumn’ General graph optimization and matlab implementation results

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그림 7. ‘Stairs’ 일반 그래프 최적화 및 매트랩 구동 결과 Fig. 7 ‘Stairs’ General graph optimization and matlab implementation results

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그림 8. ‘Garage’ 일반 그래프 최적화 및 매트랩 구동 결과 Fig. 8 ‘Garage’ General graph optimization and matlab implementation results

Acknowledgement

Supported by : 조선대학교

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