A Brief Survey on SLAM Methods in Autonomous Vehicle

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    The overall purpose of this paper is to provide an introductory survey in the area of Simultaneous Localization and Mapping (SLAM) particularly its utilization in autonomous vehicle or more specifically in self-driving cars, especially after the release of commercial semi-autonomous car like the Tesla vehicles as well as the Google Waymo vehicle. Before we begin diving into the concept of SLAM, we need to understand the importance of SLAM and problems that expand to the various methods developed by numerous researchers to solve it. Thus, in this paper we will start by giving the general concept behind SLAM, followed by sharing details of its different categories and the various methods that form the SLAM function in today’s autonomous vehicles; which can solve the SLAM problem. These methods are the current trends that are widely focused in the research community in producing solutions to the SLAM problem; not only in autonomous vehicle but in the robotics field as well. Next, we will compare each of these methods in terms of its pros and cons before concluding the paper by looking at future SLAM challenges.

     


  • Keywords


    Bayes’ theorem; Extended Kalman Filter; FastSLAM; Graph SLAM.

  • References


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Article ID: 22477
 
DOI: 10.14419/ijet.v7i4.27.22477




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