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92
Robust Monte Carlo Localization for Mobile Robots
, 2001
"... Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), whi ..."
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Cited by 826 (88 self)
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Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL algorithm, this article develops a more robust algorithm called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation. To apply this algorithm to mobile robots equipped with range finders, a kernel density tree is learned that permits fast sampling. Systematic empirical results illustrate the robustness and computational efficiency of the approach.
Markov Localization for Mobile Robots in Dynamic Environments
 Journal of Artificial Intelligence Research
, 1999
"... Localization, that is the estimation of a robot's location from sensor data, is a fundamental problem in mobile robotics. This papers presents a version of Markov localization which provides accurate position estimates and which is tailored towards dynamic environments. The key idea of Marko ..."
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Cited by 360 (48 self)
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Localization, that is the estimation of a robot's location from sensor data, is a fundamental problem in mobile robotics. This papers presents a version of Markov localization which provides accurate position estimates and which is tailored towards dynamic environments. The key idea of Markov localization is to maintain a probability density over the space of all locations of a robot in its environment. Our approach represents this space metrically, using a negrained grid to approximate densities. It is able to globally localize the robot from scratch and to recover from localization failures. It is robust to approximate models of the environment (such as occupancy grid maps) and noisy sensors (such as ultrasound sensors). Our approach also includes a ltering technique which allows a mobile robot to reliably estimate its position even in densely populated environments in which crowds of people block the robot's sensors for extended periods of time. The method described he...
Incremental mapping of large cyclic environments
 In Computational Intelligence in Robotics and Automation
, 1999
"... Mobile robots can use geometric or topological maps of their environment to navigate reliably. Automatic creation of such maps is still an unrealized goal, especially in environments that have large cyclical structures. Drawing on recent techniques of global registration and correlation, we present ..."
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Cited by 337 (19 self)
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Mobile robots can use geometric or topological maps of their environment to navigate reliably. Automatic creation of such maps is still an unrealized goal, especially in environments that have large cyclical structures. Drawing on recent techniques of global registration and correlation, we present a method, called Local Registration and Global Correlation (LRGC), for reliable reconstruction of consistent global maps from dense range data. The method is attractive because it is incremental, producing an updated map with every new sensor input; and runs in constant time independent of the size of the map (except when closing large cycles). A realtime implementation and results are presented for several indoor environments. 1.
Experiences with an Interactive Museum TourGuide Robot
, 1998
"... This article describes the software architecture of an autonomous, interactive tourguide robot. It presents a modular and distributed software architecture, which integrates localization, mapping, collision avoidance, planning, and various modules concerned with user interaction and Webbased telep ..."
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Cited by 328 (75 self)
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This article describes the software architecture of an autonomous, interactive tourguide robot. It presents a modular and distributed software architecture, which integrates localization, mapping, collision avoidance, planning, and various modules concerned with user interaction and Webbased telepresence. At its heart, the software approach relies on probabilistic computation, online learning, and anytime algorithms. It enables robots to operate safely, reliably, and at high speeds in highly dynamic environments, and does not require any modifications of the environment to aid the robot's operation. Special emphasis is placed on the design of interactive capabilities that appeal to people's intuition. The interface provides new means for humanrobot interaction with crowds of people in public places, and it also provides people all around the world with the ability to establish a "virtual telepresence" using the Web. To illustrate our approach, results are reported obtained in mid...
A maximum likelihood stereo algorithm
 Computer Vision and Image Understanding
, 1996
"... A stereo algorithm is presented that optimizes a maximum likelihood cost function. The maximum likelihood cost function assumes that corresponding features in the left and right images are Normally distributed about a common true value and consists of a weighted squared error term if two features ar ..."
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Cited by 240 (2 self)
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A stereo algorithm is presented that optimizes a maximum likelihood cost function. The maximum likelihood cost function assumes that corresponding features in the left and right images are Normally distributed about a common true value and consists of a weighted squared error term if two features are matched or a ( xed) cost if a feature is determined to be occluded. The stereo algorithm nds the set of correspondences that maximize the cost function subject to ordering and uniqueness constraints. The stereo algorithm is independent of the matching primitives. However, for the experiments described in this paper, matching is performed on the individual pixel intensities. Contrary to popular belief, the pixelbased stereo appears to be robust for a variety of images. It also has the advantages of (i) providing a dense disparity map, (ii) requiring no feature extraction and (iii) avoiding the adaptive windowing problem of areabased correlation methods. Because feature extraction and windowing are unnecessary, avery fast implementation is possible. Experimental results reveal that good stereo correspondences can be found using only ordering and uniqueness constraints, i.e. without local smoothness constraints. However, it is shown that the original maximum likelihood stereo algorithm exhibits multiple global minima. The dynamic programming algorithm is guaranteed to nd one, but not necessarily the same one for each epipolar scanline causing erroneous
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 ..."
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Cited by 238 (19 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.
Dynamic map building for an autonomous mobile robot
 Int. J. Robotics Research
, 1992
"... This article presents an algorithm for autonomous map building and maintenance for a mobile robot. We believe that mobile robot navigation can be treated as a problem of tracking geometric features that occur naturally in the environment. We represent each feature in the map by a location estimate ..."
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Cited by 202 (5 self)
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This article presents an algorithm for autonomous map building and maintenance for a mobile robot. We believe that mobile robot navigation can be treated as a problem of tracking geometric features that occur naturally in the environment. We represent each feature in the map by a location estimate (the feature state vector) and two distinct measures of uncertainty: a covariance matrix to represent uncertainty in feature location, and a credibility measure to represent our belief in the validity of the feature. During each position update cycle, predicted measurements are generated for each geometric feature in the map and compared with actual sensor observations. Successful matches cause a feature’s credibility to be increased. Unpredicted observations are used to initialize new geometric features, while unobserved predictions result in a geometric feature’s credibility being decreased. We describe experimental results obtained with the algorithm that demonstrate successful map building using real sonar data. 1.
Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids
 In Proceedings of the Thirteenth National Conference on Artificial Intelligence, Menlo Park
, 1996
"... In order to reuse existing models of the environment mobile robots must be able to estimate their position and orientation in such models. Most of the existing methods for position estimation are based on special purpose sensors or aim at tracking the robot's position relative to the known sta ..."
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Cited by 200 (47 self)
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In order to reuse existing models of the environment mobile robots must be able to estimate their position and orientation in such models. Most of the existing methods for position estimation are based on special purpose sensors or aim at tracking the robot's position relative to the known starting point. This paper describes the position probability grid approach to estimating the robot's absolute position and orientation in a metric model of the environment. Our method is designed to work with standard sensors and is independent of any knowledge about the starting point. It is a Bayesian approach based on certainty grids. In each cell of such a grid we store the probability that this cell refers to the current position of the robot. These probabilities are obtained by integrating the likelihoods of sensor readings over time. Results described in this paper show that our technique is able to reliably estimate the position of a robot in complex environments. Our approach has proven to...
Probabilistic Algorithms and the Interactive Museum TourGuide Robot Minerva
, 2000
"... This paper describes Minerva, an interactive tourguide robot that was successfully deployed in a Smithsonian museum. Minerva's software is pervasively probabilistic, relying on explicit representations of uncertainty in perception and control. This article describes ..."
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Cited by 192 (39 self)
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This paper describes Minerva, an interactive tourguide robot that was successfully deployed in a Smithsonian museum. Minerva's software is pervasively probabilistic, relying on explicit representations of uncertainty in perception and control. This article describes
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 ..."
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Cited by 113 (19 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.