원문정보
보안공학연구지원센터(IJGDC)
International Journal of Grid and Distributed Computing
Vol.9 No.12
2016.12
pp.143-158
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보