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Informations générales |
| Auteur |
Uršič, Peter; Tabernik, Domen; Boben, Marko; Skočaj, Danijel; Leonardis, Aleš; Kristan, Matej |
| Publié |
InTech Open Access Publisher, 2013
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| Abstract |
For successful operation in real‐world
environments, a mobile robot requires an effective spatial
model. The model should be compact, should possess
large expressive power and should scale well with
respect to the number of modelled categories. In this
paper we propose a new compositional hierarchical
representation of space that is based on learning
statistically significant observations, in terms of the
frequency of occurrence of various shapes in the
environment. We have focused on a two‐dimensional
space, since many robots perceive their surroundings in
two dimensions with the use of a laser range finder or
sonar. We also propose a new low‐level image descriptor,
by which we demonstrate the performance of our
representation in the context of a room categorization
problem. Using only the lower layers of the hierarchy, we
obtain state‐of‐the‐art categorization results in two
different experimental scenarios. We also present a large,
freely available, dataset, which is intended for room
categorization experiments based on data obtained with a
laser range finder. |
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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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