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GPU-based heuristic escape for outdoor large scale registration
Yin P(殷鹏); Gu F(谷丰); Li DC(李德才); He YQ(何玉庆); Yang LY(杨丽英); Han JD(韩建达)
Department机器人学研究室
Conference Name2016 IEEE International Conference on Real-time Computing and Robotics (IEEE RCAR 2016)
Conference DateJune 6-10, 2016
Conference PlaceAngkor Wat, Cambodia.
Source Publication2016 IEEE International Conference on Real-time Computing and Robotics (IEEE RCAR 2016)
PublisherIEEE
Publication PlaceNew York
2016
Pages260-265
Indexed ByEI ; CPCI(ISTP)
EI Accession number20170403280291
WOS IDWOS:000391370200046
Contribution Rank1
ISBN978-1-4673-8959-4
AbstractHeterogeneous robot introduce a higher perception ability than single type robots in outdoor environments. One key problem is to making the 3D environmental model from the cooperated robots in real time, especially in the unstructured environment. Based on our previous work on outdoor environment registration method, in this paper, we introduce a GPU based Enhanced ICP method for large-scale heterogeneous robot registration. First, we combine the GPU-based nearest neighbor search in the traditional ICP framework. Second, we proposed a measurement and estimation model for the local minima problem. Third, we proposed a GPU-based heuristic escape method to generate the escaping transformation in real time. Experiments involving one unmanned aerial vehicle and one unmanned surface vehicle were conducted to verify the proposed technique. The experimental results were compared with those of normal ICP registration algorithms to demonstrate the performance of the proposed method.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/19556
Collection机器人学研究室
Corresponding AuthorYin P(殷鹏)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
2.University of Chinese Academy of Sciences, Beijing, 100049, China
Recommended Citation
GB/T 7714
Yin P,Gu F,Li DC,et al. GPU-based heuristic escape for outdoor large scale registration[C]. New York:IEEE,2016:260-265.
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