In order to search and rescue the victims in rubble effective/): a 3-0 mop of the rubble is required. As a part of the national project of rescue mbot qstem, we are inuestigating a method for constructing a 3-0 map of rubble by teleoperated mobile mbots. We are also planning to build an intuitive user interface for teleoperating robots and navigating in a virtualized rubble modelusing the obtained 3-0 model. In this paper; some preliminary research results are intmduced. We did some design studies of laser rangejnders rhat con be mounted on U mobile mbot ond can get the range datu of the rubble amund the mbot. Then, we formulated a 3 0 S U M (Simultaneous Localization and Map BuildingJ algorirhm and conducted some simulation studies. Lasrlx we pmposed a novel morion canceling camera system and cor@"d its validiry by experiment.
Conventional topic segmentation utilizes cosine measure as the similarity between consecutive passages. However, the cosine measure has a problem that it can not reflect the similarity unless exactly the same words are included in the passages. To solve this problem, in this paper, we propose a method to acquire the word similarity between different words from the input data directly and automatically by managing to collect the same topic sections. Further more, we propose a method to compute the passage similarity based on the word similarity. Finally we propose a method of topic segmentation based on the passage similarity in an unsupervised mode.
TV viewers want to grasp the contents of the news program in a short time due to the increasing number of news channels. Conventional summarization methods based on extraction of the important sentences from each topic included in the news speech is insufficient because the important sentences can not always be extracted from each topic due to unknown topic boundary. To solve this problem, in this paper, we propose a summarization method of TV news program by segmenting the news speech into topics and then extracting the important sentence from each topic.
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