2008-01-01Zeitschriftenartikel DOI: 10.18452/9421
Constraint Based World Modeling
Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II
Common approaches for robot navigation use Bayesian ﬁlters like particle ﬁlters, Kalman ﬁlters and their extended forms. We present an alternative and supplementing approach using con- straint techniques based on spatial constraints between object positions. This yields several advan- tages. The robot can choose from a variety of belief functions, and the computational complexity is decreased by efﬁcient algorithms. The paper investigates constraint propagation techniques under the special requirements of navigation tasks. Sensor data are noisy, but a lot of redundancies can be exploited to improve the quality of the result. We introduce two quality measures: The ambiguity measure for constraint sets deﬁnes the precision, while inconsistencies are measured by the incon- sistency measure. The measures can be used for evaluating the available data and for computing best ﬁtting hypothesis. A constraint propagation algorithm is presented.
Dateien zu dieser Publikation