1The study of stochastic models for Simultaneous Localization and Map Building (SLAM) in mobile robotics has been an active research topic for over fifteen years. Within the Kalman filter (KF) approach to SLAM, seminal work (Smith and Cheeseman, 1986) suggested that as successive landmark observations take place, the correlation between the estimates of the location of such landmarks in a map g...

This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic uncertainty, and develops a rigorous methodology for efficient refinement of epistemic uncertainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The propose...

Despite significant progress in active perception methods, one of the greatest challenges toward full flight autonomy of UAVs remains the high-speed flight in unknown and unstructured environments. Leveraging the richer representation of occupancy in confidence-aware maps [1], we address the problem of planning high-speed motions in unknown environments. While considering the future map uncerta...

Environmental planning and management can have positive effects on development of some land uses including industrial areas that have a major effect on economic, social and environmental conditions. Considering the most important problems associated with modeling, the fundamental methods and functions of site-selection laid inside the geographical information system are not accounted for the mu...

Spatiotemporally integrated fractionation involves finding a fluence-map and a number of treatment sessions that maximize tumor-kill subject to dose-limits on organs-at-risk (OAR). This problem was recently formulated using the linear-quadratic dose-response model. Owing to the uncertainty in dose-response parameters, however, a solution presumed optimal might be infeasible in practice. We addr...

Motion eld segmentation is an important stage in a real-time object tracking. The multi-point optical ow approach is a means to achieve this, but the approach is limited by uncertainty. This paper presents a motion eld segmentation scheme which incorporates the multiresolution clustering and the multi-point method to overcome the uncertainty. This scheme produces a multireso-lution segmentation...

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...

During emergency response, individuals observe only part of the picture, sharing of information is needed to get the required complete picture. The aim of our study is to get insight in the collaborative mapping process in order to derive requirements for a map-sharing tool. First, we analyzed the domain to assess the mapping processes, to identify general problem areas of the assessed processe...

In this study, we build a causal map of the investment decision using causal mapping techniques by interviewing venture capitalists (VCs). We convert this map into a causal Bayes net using techniques we developed. Causal Bayes nets are especially suited for domains characterized by high degree of uncertainty. Bayes nets have been recently developed in artificial intelligence and used in medical...

In this paper we derive analytical upper bounds on the covariance of the state estimates in SLAM. The analysis is based on a novel formulation of the SLAM problem, which enables the simultaneous estimation of the landmark coordinates with respect to a robot-centered frame (relative map), as well as with respect to a fixed global frame (absolute map). A study of the properties of the covariance ...