Abstract
Spatial information in autonomous robot tasks is uncertain due to measurement errors, the dynamic nature of the world, and an incompletely known environment. We present a probabilistic spatial data model capable of describing relevant spatial data, such as object location, shape, composition, and other parameters, in the presence of uncertainty. Uncertain spatial information is modeled through continuous probability distributions on values of attributes. The data model is designed to support our visual tracking and navigation prototype.
This work is partially supported by the Paul Ivanier Center for Robotics and Production Management, Ben-Gurion University.
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Kornatzky, Y., Shimony, S.E. (1993). A probabilistic spatial data model. In: Mařík, V., Lažanský, J., Wagner, R.R. (eds) Database and Expert Systems Applications. DEXA 1993. Lecture Notes in Computer Science, vol 720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57234-1_30
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DOI: https://doi.org/10.1007/3-540-57234-1_30
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