Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (9): 223-234.doi: 10.11896/jsjkx.200700152

• Artificial Intelligence • Previous Articles     Next Articles

Overview of SLAM Algorithms for Mobile Robots

TIAN Ye, CHEN Hong-wei, WANG Fa-sheng, CHEN Xing-wen   

  1. Department of Information and Communication,Dalian Minzu University,Dalian,Liaoning 116000,China
  • Received:2020-07-24 Revised:2020-09-23 Online:2021-09-15 Published:2021-09-10
  • About author:TIAN Ye,born in 1996,postgraduate.His main research direction include robot and SLAM algorithm.
    CHEN Hong-wei,born in 1981,Ph.D,lecturer.His main research direction include robot and SLAM algorithm,etc.
  • Supported by:
    Natural Science Foundation Guidance Plan Project of Liaoning Province,China(2019-ZD-0171)

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Abstract: As a localization and map construction method,SLAM(Simultaneous Localization and Mapping) is widely used in the field of robots.SLAM algorithm enables the robot to perceive environmental information and establish environmental map through sensors carried by the robot itself in an unfamiliar environment,and calculate its own posture.In this way,the robot can move in an unknown environment.With the in-depth study of SLAM,the research results in the field of SLAM have been very rich.However,the discussion on indoor SLAM is not comprehensive enough.Through the summary and comparison of the exis-ting development results of the SLAM method,a comprehensive statement is shown.In this paper,the technical status of SLAM and the classification problem of SLAM under different sensors in indoor scenes are firstly introduced.Secondly,the classic framework of SLAM is revealed.Thirdly,the principles of SLAM algorithms with different sensors are described according to the different types of related sensors.Fourthly,the limitations of the traditional indoor SLAM algorithms are discussed and two research directions-SLAM based on multi-sensor fusion technology and SLAM based on deep learning technology are led out.Finally,the future development trend and application field of SLAM are suggested

Key words: Camera, Deep learning, Indoor, Lidar, Multi-sensor, Positioning and mapping

CLC Number: 

  • TP242
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