Large volume metrology instrument selection and measurability analysis

Metrology processes used in the manufacture of large products include tool setting, product verification and flexible metrology enabled automation. The range of applications and instruments available makes the selection of the appropriate instrument for a given task highly complex. Since metrology is a key manufacturing process it should be considered in the early stages of design. This paper provides an overview of the important selection criteria for typical measurement processes and presents some novel selection strategies. Metrics which can be used to assess measurability are also discussed. A prototype instrument selection and measurability analysis application is presented with discussion of how this can be used as the basis for development of a more sophisticated measurement planning tool. © Springer-Verlag Berlin Heidelberg 2010.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.