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133
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 839 (85 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.
Dynamic Bayesian Networks: Representation, Inference and Learning
, 2002
"... Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and biosequence analysis, and KFMs have bee ..."
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Cited by 770 (3 self)
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Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and biosequence analysis, and KFMs have been used for problems ranging from tracking planes and missiles to predicting the economy. However, HMMs
and KFMs are limited in their “expressive power”. Dynamic Bayesian Networks (DBNs) generalize HMMs by allowing the state space to be represented in factored form, instead of as a single discrete random variable. DBNs generalize KFMs by allowing arbitrary probability distributions, not just (unimodal) linearGaussian. In this thesis, I will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in DBNs, and how to learn DBN models from sequential data.
In particular, the main novel technical contributions of this thesis are as follows: a way of representing
Hierarchical HMMs as DBNs, which enables inference to be done in O(T) time instead of O(T 3), where T is the length of the sequence; an exact smoothing algorithm that takes O(log T) space instead of O(T); a simple way of using the junction tree algorithm for online inference in DBNs; new complexity bounds on exact online inference in DBNs; a new deterministic approximate inference algorithm called factored frontier; an analysis of the relationship between the BK algorithm and loopy belief propagation; a way of
applying RaoBlackwellised particle filtering to DBNs in general, and the SLAM (simultaneous localization
and mapping) problem in particular; a way of extending the structural EM algorithm to DBNs; and a variety of different applications of DBNs. However, perhaps the main value of the thesis is its catholic presentation of the field of sequential data modelling.
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 361 (45 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...
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 329 (72 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 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 239 (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.
Probabilistic Algorithms in Robotics
 AI Magazine vol
"... This article describes a methodology for programming robots known as probabilistic robotics. The probabilistic paradigm pays tribute to the inherent uncertainty in robot perception, relying on explicit representations of uncertainty when determining what to do. This article surveys some of the progr ..."
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Cited by 199 (6 self)
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This article describes a methodology for programming robots known as probabilistic robotics. The probabilistic paradigm pays tribute to the inherent uncertainty in robot perception, relying on explicit representations of uncertainty when determining what to do. This article surveys some of the progress in the field, using indepth examples to illustrate some of the nuts and bolts of the basic approach. Our central conjecture is that the probabilistic approach to robotics scales better to complex realworld applications than approaches that ignore a robot’s uncertainty. 1
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 196 (38 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
An experimental comparison of localization methods
, 1998
"... Localization is the process of updating the pose of a robot in an environment, based on sensor readings. In this experimental study, we compare two recent methods for localization of indoor mobile robots: Markov localization, which uses a probability distribution across a grid of robot poses; and sc ..."
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Cited by 185 (42 self)
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Localization is the process of updating the pose of a robot in an environment, based on sensor readings. In this experimental study, we compare two recent methods for localization of indoor mobile robots: Markov localization, which uses a probability distribution across a grid of robot poses; and scan matching, which uses Kalman filtering techniques based on matching sensor scans. Both these techniques are dense matching methods, that is, they match dense sets of environment features to an a priori map. To arrive at results for a range of situations, we utilize several different types of environments, and add noise to both the deadreckoning and the sensors. Analysis shows that, roughly, the scanmatching techniques are more efficient and accurate, but Markov localization is better able to cope with large amounts of noise. These results suggest hybrid methods that are efficient, accurate and robust to noise. 1.
Distributed multirobot localization
 IEEE Transactions on Robotics and Automation
, 2002
"... Abstract. This paper presents a new approach to the cooperative localization problem, namely distributed multirobot localization. A group of M robots is viewed as a single system composed of robots that carry, in general, di erent sensors and have di erent positioning capabilities. A single Kalman ..."
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Cited by 180 (20 self)
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Abstract. This paper presents a new approach to the cooperative localization problem, namely distributed multirobot localization. A group of M robots is viewed as a single system composed of robots that carry, in general, di erent sensors and have di erent positioning capabilities. A single Kalman lter is formulated to estimate the position and orientation of all the members of the group. This centralized schema is capable of fusing information provided by the sensors distributed on the individual robots while accommodating independencies and interdependencies among the collected data. In order to allow for distributed processing, the equations of the centralized Kalman lter are treated so that this lter can be decomposed into M modi ed Kalman lters each running on a separate robot. The distributed localization algorithm is applied to a group of 3 robots and the improvement in localization accuracy is presented. 1
Using the CONDENSATION Algorithm for Robust, Visionbased Mobile Robot Localization
, 1999
"... To navigate reliably in indoor environments, a mobile robot must know where it is. This includes both the ability of globally localizing the robot from scratch, as well as tracking the robot's position once its location is known. Vision has long been advertised as providing a solution to these ..."
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Cited by 137 (25 self)
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To navigate reliably in indoor environments, a mobile robot must know where it is. This includes both the ability of globally localizing the robot from scratch, as well as tracking the robot's position once its location is known. Vision has long been advertised as providing a solution to these problems, but we still lack efficient solutions in unmodified environments. Many existing approaches require modification of the environment to function properly, and those that work within unmodified environments seldomly address the problem of global localization. In this paper we present a novel, visionbased localization method based on the CONDENSATION algorithm [17, 18], a Bayesian filtering method that uses a samplingbased density representation. We show how the CONDENSATION algorithm can be used in a novel way to track the position of the camera platform rather than tracking an object in the scene. In addition, it can also be used to globally localize the camera platform, given a visua...