Earticle

초록

영어

Compute Unified Device Architecture (CUDA) is a mature parallel computing architecture, which can significantly accelerate performance of the computation intensive algorithm. In this paper, FastSLAM algorithm based on the probability model is further studied and the resampling algorithm for the path estimation is improved. In the resampling phase, resampling rules are redesigned and the previous data limitations are broken for the purpose of parallelization. We propose the FastSLAM algorithm based on CUDA, which accelerates robot localization and mapping. The experiment results show that FastSLAM_CUDA can achieve a significant speedup over the FastSLAM with many particles.

목차

Abstract
 1. Introduction
 2. CUDA Technology
 3. FastSLAM Algorithm
  3.1 Probabilistic SLAM
  3.2 FastSLAM
 4. FastSLAM Algorithm Based on CUDA
  4.1 Algorithm Description of Resampling
  4.2 Analysis and Improvement of Parallelization
 5. Simulation Experiment and Analysis
  5.1 Experimental Object and Experimental Environment
  5.2 Experimental Results and Analysis
 6. Conclusion
 References

키워드

저자

• Heng Zhang [ School of Information Engineering, East China Jiaotong University, Nanchang, China ]
• Yanli Liu [ School of Information Engineering, East China Jiaotong University, Nanchang, China ]
• Mengyu Zhu [ School of Information Engineering, East China Jiaotong University, Nanchang, China ]
• Naixue Xiong [ School of Computer, Hubei University of Education, Wuhan, P.R. of China / Dept. of Business and Computer Science, Southwestern Oklahoma State University, USA ]
• Tai-hoon Kim [ Department of Multimedia Engineering, Hannam University, Daejeon, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605