Multi-granularity and metric spatial reasoning

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• 作者：Shengsheng Wang ; Yiting Liu ; Dayou Liu ; Bolou Bolou Dickson ; Xinying Wang
• 关键词：Qualitative spatial reasoning ; Multi-granularity spatial reasoning ; Metric spatial reasoning ; Weak composition ; Oriented point relation algebra
• 刊名：Expert Systems with Applications
• 出版年：May, 2014
• 年：2014
• 卷：41
• 期：6
• 页码：3116-3133
• 全文大小：2306 K

文摘

Composition reasoning is a basic reasoning task in qualitative spatial reasoning (QSR). It is an important qualitative method for robot navigation, node localization in wireless sensor networks and other fields. The previous composition reasoning works dedicated in single granularity framework. Multi-granularity spatial relation is not rare in real world, and some qualitative spatial relation models are multi-granularity models, such as RCC, STAR_m, CDC_m and OPRA_m. Although multi-granularity composition reasoning is very useful in many applications, it has not been systematically studied before. A special case of multi-granularity composition reasoning, referred to as metric spatial reasoning, is also discussed here. The general frameworks and basic theories for multi-granularity and metric spatial reasoning are put forward here. Furthermore, we redefine the spatial relation models for distance, topology and direction under the proposed multi-granularity and metric frameworks. We add metric representation for the OPRA_m. The multi-granularity and metric reasoning tasks are studied for these four models for the first time. Finally we perform some experiments on OPRA_m with encouraging results to verify our theories. Multi-granularity and metric spatial reasoning tasks are new problems in QSR and quite different from the previous works. Our works can be potentially applied in robot navigation, wireless sensor networks and other applications.