We describe a method of mobile robot localization based on a rough map using stereo vision, which uses multiple visual features to detect and segment the buildings in the robot’s field of view. The rough map is an inaccurate map with large uncertainties in the shapes, dimensions and locations of objects so that it can be built easily. The robot fuses odometry and vision information using extend...

In this paper we investigate whether macroeconomic uncertainty could distort banks’ allocation of loanable funds. To provide a road– map for our empirical investigation, we present a simple framework which demonstrates that lower uncertainty about the return from lending should lead to a more unequal distribution of lending across banks as managers take advantage of more precise knowledge of di...

Usually, in real-world engineering problems, there are different types of uncertainties about the studied variables, which can be due to the specific variables under investigation or interaction between them. Fuzzy cognitive maps, which addresses the cause-effect relation between variables, is one of the most common models for better understanding of the problems, especially when the quantitati...

We study prediction of chaotic time series when a perfect model is available but the initial condition is measured with uncertainty. A common approach for predicting future data given these circumstances is to apply the model despite the uncertainty. In systems with fold dynamics, we find prediction is improved over this strategy by recognizing this behavior. A systematic study of the Logistic ...

This paper presents a localization strategy for robotic assemblies with position uncertainty. The assembly of parts whose position uncertainty exceeds assembly clearance has to rely on either visual assistance or searching to achieve parts mating. We present a general strategy, applicable to arbitrary peg-in-hole assemblies, that localizes the misalignment of the mating parts in an efficient ma...

Predictive modeling of multiscale and multiphysics systems requires accurate data-driven characterization of the input uncertainties and understanding how they propagate across scales and alter the final solution. We will address three major current limitations in modeling stochastic systems: (1) Most of current uncertainty quantification methods cannot detect and handle discontinuity in the pa...

The uncertainty in parameter estimation due to the adverse environments deteriorates the classification performance for speech recognition. It becomes crucial to incorporate the parameter uncertainty into decision so that the classification robustness can be assured. In this paper, we propose a novel linear regression based Bayesian predictive classification (LRBPC) for robust speech recognitio...