Both theoretical and practical problems in deep learning classification require solutions for assessing uncertainty prediction but current state-of-the-art methods this area are computationally expensive. In paper, we propose a new confidence measure dubbed Over-MAP that utilizes of overlap between structural attention mechanisms segmentation methods, is particular interest accurate fine-graine...

The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from the estimation-theoretic foundations of this problem developed in [1]–[3], this pa...

Modelling spatial variability and uncertainty is a highly challenging subject in soiland geosciences. Regression kriging (RK) has several advantages; nevertheless it is not able to model the spatial uncertainty of the target variable. The main aim of this study is to present and test a sequential stochastic simulation approach based on regression kriging (SSSRK), which can be used to generate a...

Nearly all spatial reasoning problems in­ volve uncertainty of one sort or another. Uncertainty arises due to the inaccuracies of sensors used in measuring distances and angles. We refer to this as directional un­ certainty. Uncertainty also arises in com­ bining spatial information when one loca­ tion is mistakenly identified with another. We refer to this as recognition uncertainty. Most prob...

In this paper we investigate robot localization with the Augmented Monte Carlo Localization (aMCL) algorithm. The goal of the algorithm is to enable a robot to localize itself in an known world. The map of the environment where the robot has to localize itself must be given to the robot beforehand. In our case the map is constructed by another robot. This map is a so called occupancy grid. An o...

To fully comprehend the meaning and impact of visualized data it is crucial that users are able to perceive and comprehend the inherent uncertainty of the data in a correct and intuitive way. Data uncertainty is frequently visualized through color mappings. Previous studies argued that color hue is not suitable for communicating uncertainty because most hue scales lack an intrinsic perceptual o...

This paper concentrates on the study on the map fusion problem in the context of a multi-robot map building approach. Concretely it is seen as one of the steps towards the independent multi-robot map building. In the situation proposed a set of several robots performs map building tasks without the notion of other robots’ existence. Each robot builds its own local map using its observations and...

In pest risk assessment it is frequently necessary to make management decisions regarding emerging threats under severe uncertainty. Although risk maps provide useful decision support for invasive alien species, they rarely address knowledge gaps associated with the underlying risk model or how they may change the risk estimates. Failure to recognize uncertainty leads to risk-ignorant decisions...