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Parametric And Nonparametric Approaches For Multisensor Data Fusion
, 2001
"... Multisensor data fusion technology combines data and information from multiple sensors to achieve improved accuracies and better inference about the environment than could be achieved by the use of a single sensor alone. In this dissertation, we propose parametric and nonparametric multisensor data ..."
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Multisensor data fusion technology combines data and information from multiple sensors to achieve improved accuracies and better inference about the environment than could be achieved by the use of a single sensor alone. In this dissertation, we propose parametric and nonparametric multisensor data fusion algorithms with a broad range of applications. Image registration is a vital first step in fusing sensor data. Among the wide range of registration techniques that have been developed for various applications, mutual information based registration algorithms have been accepted as one of the most accurate and robust methods. Inspired by the mutual information based approaches, we propose to use the joint Renyi entropy as the dissimilarity metric between images. Since the Renyi entropy of an image can be estimated with the length of the minimum spanning tree over the corresponding graph, the proposed informationtheoretic registration algorithm can be implemented by a novel nonparametric graphrepresentation method. The image matching is performed by minimizing the length of the minimum spanning tree (MST) which spans the graph generated from the overlapping images. Our method also takes advantage of the minimum kpoint spanning tree (kMST) approach to robustify the registration against outliers in the images. Since this algorithm does not require any parametric model, it can be directly applied to a variety of image types. We also propose a parametric sensor fusion algorithm for simultaneous lane and pavement boundary detection in registered optical and radar images. The fusion problem is formulated in a Bayesian setting where the deform...
Coherent Functions in Autonomous Systems
, 2002
"... INTRODUCTION Advanced sensorimotor devices, like mobile robots, are often referred to as autonomous systems. The expression is used to intentionally remark on the difference between these systems and those of traditional industrial automation. Although no rigorous definition is easily available, a ..."
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Cited by 3 (2 self)
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INTRODUCTION Advanced sensorimotor devices, like mobile robots, are often referred to as autonomous systems. The expression is used to intentionally remark on the difference between these systems and those of traditional industrial automation. Although no rigorous definition is easily available, a general informal consensus seems to exist on the features that denote autonomy: a system is considered the more autonomous the more reliably it can survive and perform tasks in the real world, without the need for human intervention. 171 ____________________________ *email: claudio.sossai@isib.cnr.it J. of Mult.Valued Logic & Soft Computing., Vol. 9, pp. 171194 2003 Old City Publishing, Inc. Reprints available directly from the publisher Published by license under the OCP Science imprint, Photocopying permitted by license only a member of the Old City Publishing Group In the literature different ideas and techniques have been proposed and investigated to achieve these results, nonethe
The design of a pair of identical mobile robots to investigate cooperative behaviours
 Cutting Edge Robotics – Section V: MultiRobot Systems
, 2005
"... When moving large or heavy items, the traditional tendency with machinery is to build a large mechanism capable of handling the load. This leads to continuous scaling of the mechanical size and weight of devices being built, with a proportional increase in expense. ..."
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When moving large or heavy items, the traditional tendency with machinery is to build a large mechanism capable of handling the load. This leads to continuous scaling of the mechanical size and weight of devices being built, with a proportional increase in expense.
Fusion of Symbolic Knowledge and Uncertain Information in Robotics
 International Journal of Intelligent Systems
, 2001
"... The interpretation of data coming from the real world may require different and often complementary uncertainty models: some are better described by possibility theory, others are intrinsically probabilistic. A logic for belief functions is introduced to axiomatize both semantics as special cases. A ..."
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The interpretation of data coming from the real world may require different and often complementary uncertainty models: some are better described by possibility theory, others are intrinsically probabilistic. A logic for belief functions is introduced to axiomatize both semantics as special cases. As it properly extends classical logic, it also allows the fusion of data with different semantics and symbolic knowledge. The approach has been applied to the problem of mobile robot localization. For each place in the environment, a set of logical propositions allows the system to calculate the belief of the robot's presence as a function of the partial evidences provided by the individual sensors.
Merging Probability and Possibility for Robot Localization
 In Proceedings of the Workshop on Reasoning with Uncertainty in Robot Navigation (RUR99
, 1999
"... A mobile robot localization system based on sensor fusion is described. Data coming from various sensors can require di#erent and often complementary uncertainty models: some are better described by possibility theory, others are intrinsically probabilistic. A logic for belief functions is int ..."
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Cited by 1 (0 self)
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A mobile robot localization system based on sensor fusion is described. Data coming from various sensors can require di#erent and often complementary uncertainty models: some are better described by possibility theory, others are intrinsically probabilistic. A logic for belief functions is introduced to axiomatize both semantics as special cases. For each place in a map of the environment, a set of logical rules allows to calculate the belief of the robot's presence, as a function of the partial evidences provided by the individual sensors. Various experimental runs have shown promising results. 1 Introduction The aim of this work is to apply to robotics a logical framework where di#erent uncertainty models, like belief functions, possibilities (i.e. consonant belief functions) and probabilities (i.e. additive belief functions) can be uniformly axiomatized. 1.1 Uncertainty models Although probability theory has a leading position in the description of uncertainty, in th...
A Note on the Role of Logic in Fuzzy Logic Controllers
, 2000
"... We give an account of (Mamdanitype) fuzzy controllers based on a version of possibilistic logic, called Local Possibilistic Logic (LPL). To find a logical interpretation of fuzzy controllers, we show: (i) how to translate fuzzy statements and fuzzy control rules into LPL formulae, (ii) how to comp ..."
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We give an account of (Mamdanitype) fuzzy controllers based on a version of possibilistic logic, called Local Possibilistic Logic (LPL). To find a logical interpretation of fuzzy controllers, we show: (i) how to translate fuzzy statements and fuzzy control rules into LPL formulae, (ii) how to compute the fuzzy relation that characterizes the controller using the proof system of LPL, and (iii) how to apply this relation to a given input using the compositional rule of inference, which is derived in LPL.
POSSIBILISTIC POSITION ESTIMATION IN MOBILE ROBOTICS
"... Abstract: Using an enhanced version of possibility theory, we propose a logicbased position estimation method, fusing symbolic knowledge and interpretations of uncertain sensor data. The method is tested on a mobile robot using an annotated topological map of an indoor environment. ..."
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Abstract: Using an enhanced version of possibility theory, we propose a logicbased position estimation method, fusing symbolic knowledge and interpretations of uncertain sensor data. The method is tested on a mobile robot using an annotated topological map of an indoor environment.
logic framework q
, 2005
"... Reasoning with multiplesource information in a possibilistic ..."
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