Results 1  10
of
58
A Probabilistic Approach to Collaborative MultiRobot Localization
, 2000
"... This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a samplebased version of Markov localization, capable of localizing mobile robots in an anytime fashion. When teams of robots localize themselves in the same environment, probabilistic method ..."
Abstract

Cited by 178 (18 self)
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This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a samplebased version of Markov localization, capable of localizing mobile robots in an anytime fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and highcost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile robots equipped with cameras and laser rangefinders for detecting other robots. The results, obtained with the real robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional singlerobot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous robots collaborate during localization.
Adapting the Sample Size in Particle Filters Through KLDSampling
 International Journal of Robotics Research
, 2003
"... Over the last years, particle filters have been applied with great success to a variety of state estimation problems. In this paper we present a statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation process. ..."
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Cited by 97 (8 self)
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Over the last years, particle filters have been applied with great success to a variety of state estimation problems. In this paper we present a statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation process.
Particle Filters for Mobile Robot Localization
, 2001
"... This article describes a family of methods, known as Monte Carlo localization (MCL) (Dellaert at al. 1999b, Fox et al. 1999b). The MCL algorithm is a particle filter combined with probabilistic models of robot perception and motion. Building on this, we will describe a variation of MCL which uses a ..."
Abstract

Cited by 94 (18 self)
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This article describes a family of methods, known as Monte Carlo localization (MCL) (Dellaert at al. 1999b, Fox et al. 1999b). The MCL algorithm is a particle filter combined with probabilistic models of robot perception and motion. Building on this, we will describe a variation of MCL which uses a different proposal distribution (a mixture distribution) that facilitates fast recovery from global localization failures. As we will see, this proposal distribution has a range of advantages over that used in standard MCL, but it comes at the price that it is more difficult to implement, and it requires an algorithm for sampling poses from sensor measurements, which might be difficult to obtain. Finally, we will present an extension of MCL to cooperative multirobot localization of robots that can perceive each other during localization. All these approaches have been tested thoroughly in practice. Experimental results are provided to demonstrate their relative strengths and weaknesses in practical robot applications.
`NBody' Problems in Statistical Learning
, 2001
"... We present efficient algorithms for allpointpairs problems, or 'Nbody 'like problems, which are ubiquitous in statistical learning. We focus on six examples, including nearestneighbor classification, kernel density estimation, outlier detection, and the twopoint correlation. ..."
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Cited by 90 (12 self)
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We present efficient algorithms for allpointpairs problems, or 'Nbody 'like problems, which are ubiquitous in statistical learning. We focus on six examples, including nearestneighbor classification, kernel density estimation, outlier detection, and the twopoint correlation.
Very Fast EMbased Mixture Model Clustering Using Multiresolution kdtrees
 In Advances in Neural Information Processing Systems 11
, 1998
"... Clustering is importantinmany fields including manufacturing, biology, finance, and astronomy. Mixture models are a popular approach due to their statistical foundations, and EM is a very popular method for finding mixture models. EM, however, requires many accesses of the data, and thus has bee ..."
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Cited by 89 (4 self)
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Clustering is importantinmany fields including manufacturing, biology, finance, and astronomy. Mixture models are a popular approach due to their statistical foundations, and EM is a very popular method for finding mixture models. EM, however, requires many accesses of the data, and thus has been dismissed as impractical (e.g. (Zhang, Ramakrishnan, & Livny, 1996)) for data mining of enormous datasets.
The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data
 In Twelfth Conference on Uncertainty in Artificial Intelligence
, 2000
"... This paper is about metric data structures in highdimensional or nonEuclidean space that permit cached sufficient statistics accelerations of learning algorithms. ..."
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Cited by 75 (8 self)
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This paper is about metric data structures in highdimensional or nonEuclidean space that permit cached sufficient statistics accelerations of learning algorithms.
Predictive ApplicationPerformance Modeling in a Computational Grid Environment
, 1999
"... This paper describes and evaluates the application of three local learning algorithms  nearestneighbor, weightedaverage, and locallyweighted polynomial regression  for the prediction of runspecific resourceusage on the basis of runtime input parameters supplied to tools. A twolevel knowl ..."
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Cited by 60 (12 self)
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This paper describes and evaluates the application of three local learning algorithms  nearestneighbor, weightedaverage, and locallyweighted polynomial regression  for the prediction of runspecific resourceusage on the basis of runtime input parameters supplied to tools. A twolevel knowledge base allows the learning algorithms to track shortterm fluctuations in the performance of computing systems, and the use of instance editing techniques improves the scalability of the performancemodeling system. The learning algorithms assist PUNCH, a networkcomputing system at Purdue University, in emulating an ideal user in terms of its resource management and usage policies. 1. Introduction It is now recognized that the heterogeneous nature of the networkcomputing environment cannot be effectively exploited without some form of adaptive or demanddriven resource management (e.g., [10, 11, 12, 14, 18, 27]). A demanddriven resource management system can be characterized by its a...
Collaborative MultiRobot Localization
, 1999
"... . This paper presents a probabilistic algorithm for collaborative mobile robot localization. Our approach uses a samplebased version of Markov localization, capable of localizing mobile robots in an anytime fashion. When teams of robots localize themselves in the same environment, probabilistic ..."
Abstract

Cited by 41 (10 self)
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. This paper presents a probabilistic algorithm for collaborative mobile robot localization. Our approach uses a samplebased version of Markov localization, capable of localizing mobile robots in an anytime fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and highcost sensors are amortized across multiple robot platforms. The paper also describes experimental results obtained using two mobile robots. The robots detect each other and estimate their relative locations based on computer vision and laser rangefinding. The results, obtained in an indoor office environment, illustrate drastic improvements in localization speed and accuracy when compared to conventional singlerobot localization. 1 Introduction Sensorbased robot localization has been recognized as one ...
Inductive Databases and Condensed Representations for Data Mining
, 1997
"... Knowledge discovery in databases and data mining aim at semiautomatic tools for the analysis of large data sets. It can be argued that several data mining tasks consist of locating interesting sentences from a given logic that are true in the database. Then the task of the user/analyst is to is to q ..."
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Cited by 36 (2 self)
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Knowledge discovery in databases and data mining aim at semiautomatic tools for the analysis of large data sets. It can be argued that several data mining tasks consist of locating interesting sentences from a given logic that are true in the database. Then the task of the user/analyst is to is to query this set, the theory of the database. This view gives rise to the concept of of inductive databases, i.e., databases that in addition to the data contain also inductive generalizations about the data. We describe a rough framework for inductive databases, and consider also condensed representations, data structures that make it possible to answer queries about the inductive database approximately correctly and reasonably efficiently. 1 Introduction Knowledge discovery in databases (KDD), often called data mining, aims at the discovery of useful information from large collections of data. The discovered knowledge can be rules describing properties of the data, frequently occurring patte...