Journal of Guangdong University of Technology ›› 2017, Vol. 34 ›› Issue (02): 92-96.doi: 10.12052/gdutxb.160055

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Parallel Optimization and Implementation of SLAM Algorithm Based on Particle Filter

Zhu Fu-li, Zeng Bi, Cao Jun   

  1. School of Computers, Guangdong University of Technology, Guangzhou 210000, China
  • Received:2016-04-01 Online:2017-03-09 Published:2017-03-09

Abstract:

Simultaneous localization and mapping is a new type of mobile robot localization method, which can obtain data through the mobile robot's own sensors and simultaneous localization and map building in a completely unknown environment. Based on PF-SLAM algorithm, the probability distribution of the location pose is expressed by the particle set, and the calculated amount is proportional to the size of the particle set, and also the number of particles determines the algorithm's location accuracy and anti-jamming capability. At the same time, increasing the number of particles will increase the computing time, which will lead to the positioning delay and the positioning error of the mobile robot. A method is presented to improve the algorithm by using GPU parallel computing, which can reduce the calculation time, thereby to reduce the positioning error caused by positioning delay. Experimental results show that the improved algorithm of GPU parallel computing has a significant effect.

Key words: simultaneous localization and mapping, particle filter, GPU parallel computing

CLC Number: 

  • TP301

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