Kalman filtering: theory and practice
Kalman filtering: theory and practice
Navigating Mobile Robots: Systems and Techniques
Navigating Mobile Robots: Systems and Techniques
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
The Location Stack: A Layered Model for Location in Ubiquitous Computing
WMCSA '02 Proceedings of the Fourth IEEE Workshop on Mobile Computing Systems and Applications
Estimating the absolute position of a mobile robot using position probability grids
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
On a generic uncertainty model for position information
QuaCon'09 Proceedings of the 1st international conference on Quality of context
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Many context- and location-aware applications request high accuracy and availability of positioning systems. In reality however, knowledge about the current position may be incomplete or inaccurate as a result of, e.g., limited coverage. Often, position data is thus merged from a set of systems, each contributing a piece of position knowledge. Traditional sensor fusion approaches such as Kalman or Particle filters have certain demands concerning the statistical distribution and relation between position and sensor output. Negated position statements ("I'm not at home"), cell-based information or external spatial data are difficult to incorporate into existing mechanisms. In this paper, we introduce a new approach to deal with different types of position data which typically appear in context- or location-aware application scenarios.