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Informations générales |
| Auteur |
Chen, Baifan; Liu, Lijue; Zou, Zhirong; Xu, Xiyang |
| Publié |
InTech Open Access Publisher, 2013
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| Abstract |
Data association is critical for Simultaneous
Localization and Mapping (SLAM). In a real
environment, dynamic obstacles will lead to false data
associations which compromise SLAM results. This paper
presents a simple and effective data association method
for SLAM in dynamic environments. A hybrid approach
of data association based on local maps by combining
ICNN and JCBB algorithms is used initially. Secondly, we
set a judging condition of outlier features in association
assumptions and then the static and dynamic features are
detected according to spatial and temporal difference.
Finally, association assumptions are updated by filtering
out the dynamic features. Simulations and experimental
results show that this method is feasible. |
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International Journal of Advanced Robotic Systems
Auteur: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Publié: 2004
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