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282
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 (89 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.
Between MDPs and SemiMDPs: A Framework for Temporal Abstraction in Reinforcement Learning
 Artificial Intelligence
, 1999
"... Learning, planning, and representing knowledge at multiple levels of temporal abstraction are key, longstanding challenges for AI. In this paper we consider how these challenges can be addressed within the mathematical framework of reinforcement learning and Markov decision processes (MDPs). We ..."
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Cited by 545 (38 self)
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Learning, planning, and representing knowledge at multiple levels of temporal abstraction are key, longstanding challenges for AI. In this paper we consider how these challenges can be addressed within the mathematical framework of reinforcement learning and Markov decision processes (MDPs). We extend the usual notion of action in this framework to include optionsclosedloop policies for taking action over a period of time. Examples of options include picking up an object, going to lunch, and traveling to a distant city, as well as primitive actions such as muscle twitches and joint torques. Overall, we show that options enable temporally abstract knowledge and action to be included in the reinforcement learning framework in a natural and general way. In particular, we show that options may be used interchangeably with primitive actions in planning methods such as dynamic programming and in learning methods such as Qlearning.
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
 Machine Learning
, 1998
"... . This paper addresses the problem of building largescale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximumlikelihood estimation problem. It then devises a practical algorithm for generating the most likely map from ..."
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Cited by 486 (49 self)
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. This paper addresses the problem of building largescale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximumlikelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, alog with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approach. Keywords: Bayes rule, expectation maximization, mobile robots, navigation, localization, mapping, maximum likelihood estimation, positioning, probabilistic reasoning 1. Introduction Over the last two decades or so, the problem of acquiring maps in indoor environments has received considerable attention in the mobile robotics community. The problem of map building is the problem of determining the location of entitiesofinterest (such as: landmarks, obstacles), often relative to a global frame of reference (such as ...
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 342 (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...
Monte Carlo Localization: Efficient Position Estimation for Mobile Robots
 IN PROC. OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI
, 1999
"... This paper presents a new algorithm for mobile robot localization, called Monte Carlo Localization (MCL). MCL is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success. However, previous approaches were either computational ..."
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Cited by 327 (48 self)
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This paper presents a new algorithm for mobile robot localization, called Monte Carlo Localization (MCL). MCL is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success. However, previous approaches were either computationally cumbersome (such as gridbased approaches that represent the state space by highresolution 3D grids), or had to resort to extremely coarsegrained resolutions. Our approach is computationally efficient while retaining the ability to represent (almost) arbitrary distributions. MCL applies samplingbased methods for approximating probability distributions, in a way that places computation " where needed." The number of samples is adapted online, thereby invoking large sample sets only when necessary. Empirical results illustrate that MCL yields improved accuracy while requiring an order of magnitude less computation when compared to previous approaches. It is also much easier to implement...
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 321 (74 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 realtime algorithm for mobile robot mapping with applications to multirobot and 3D mapping
 In IEEE International Conference on Robotics and Automation
, 2000
"... We present an incremental method for concurrent mapping and localization for mobile robots equipped with 2D laser range finders. The approach uses a fast implementation of scanmatching for mapping, paired with a samplebased probabilistic method for localization. Compact 3D maps are generated using ..."
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Cited by 309 (37 self)
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We present an incremental method for concurrent mapping and localization for mobile robots equipped with 2D laser range finders. The approach uses a fast implementation of scanmatching for mapping, paired with a samplebased probabilistic method for localization. Compact 3D maps are generated using a multiresolution approach adopted from the computer graphics literature, fed by data from a dual laser system. Our approach builds 3D maps of large, cyclic environments in realtime. It is remarkably robust. Experimental results illustrate that accurate maps of large, cyclic environments can be generated even in the absence of any odometric data. 1
An Online Mapping Algorithm for Teams of Mobile Robots
 International Journal of Robotics Research
, 2001
"... We propose a new probabilistic algorithm for online mapping of unknown environments with teams of robots. At the core of the algorithm is a technique that combines fast maximum likelihood map growing with a Monte Carlo localizer that uses particle representations. The combination of both yields an o ..."
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Cited by 233 (16 self)
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We propose a new probabilistic algorithm for online mapping of unknown environments with teams of robots. At the core of the algorithm is a technique that combines fast maximum likelihood map growing with a Monte Carlo localizer that uses particle representations. The combination of both yields an online algorithm that can cope with large odometric errors typically found when mapping an environment with cycles. The algorithm can be implemented distributedly on multiple robot platforms, enabling a team of robots to cooperatively generate a single map of their environment. Finally, an extension is described for acquiring threedimensional maps, which capture the structure and visual appearance of indoor environments in 3D.
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 222 (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.
Algorithms for Sequential Decision Making
, 1996
"... Sequential decision making is a fundamental task faced by any intelligent agent in an extended interaction with its environment; it is the act of answering the question "What should I do now?" In this thesis, I show how to answer this question when "now" is one of a finite set of ..."
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Cited by 213 (9 self)
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Sequential decision making is a fundamental task faced by any intelligent agent in an extended interaction with its environment; it is the act of answering the question "What should I do now?" In this thesis, I show how to answer this question when "now" is one of a finite set of states, "do" is one of a finite set of actions, "should" is maximize a longrun measure of reward, and "I" is an automated planning or learning system (agent). In particular,