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Performance Simulation of Various Feature-Initialization Algorithms for Forward-Viewing Mono-Camera-Based SLAM

전방 모노카메라 기반 SLAM 을 위한 다양한 특징점 초기화 알고리즘의 성능 시뮬레이션

  • Lee, Hun (School of Electrical and Computer Engineering, Seoul National University Automation and Systems Research Institute (ASRI), Seoul National University) ;
  • Kim, Chul Hong (School of Electrical and Computer Engineering, Seoul National University Automation and Systems Research Institute (ASRI), Seoul National University) ;
  • Lee, Tae-Jae (School of Electrical and Computer Engineering, Seoul National University Automation and Systems Research Institute (ASRI), Seoul National University) ;
  • Cho, Dong-Il Dan (School of Electrical and Computer Engineering, Seoul National University Automation and Systems Research Institute (ASRI), Seoul National University)
  • 이훈 (서울대학교 전기정보공학부, 자동화시 스템공동연구소) ;
  • 김철홍 (서울대학교 전기정보공학부, 자동화시 스템공동연구소) ;
  • 이태재 (서울대학교 전기정보공학부, 자동화시 스템공동연구소) ;
  • 조동일 (서울대학교 전기정보공학부, 자동화시 스템공동연구소)
  • Received : 2016.04.19
  • Accepted : 2016.08.23
  • Published : 2016.10.01

Abstract

This paper presents a performance evaluation of various feature-initialization algorithms for forward-viewing mono-camera based simultaneous localization and mapping (SLAM), specifically in indoor environments. For mono-camera based SLAM, the position of feature points cannot be known from a single view; therefore, it should be estimated from a feature initialization method using multiple viewpoint measurements. The accuracy of the feature initialization method directly affects the accuracy of the SLAM system. In this study, four different feature initialization algorithms are evaluated in simulations, including linear triangulation; depth parameterized, linear triangulation; weighted nearest point triangulation; and particle filter based depth estimation algorithms. In the simulation, the virtual feature positions are estimated when the virtual robot, containing a virtual forward-viewing mono-camera, moves forward. The results show that the linear triangulation method provides the best results in terms of feature-position estimation accuracy and computational speed.

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