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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 ..."
Abstract

Cited by 415 (48 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 ...
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 277 (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...
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 193 (14 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.
Topological Simultaneous Localization and Mapping (SLAM): Toward Exact Localization Without Explicit Localization
 IEEE Transactions on Robotics and Automation
, 2001
"... One of the critical components of mapping an unknown environment is the robot's ability to locate itself on a partially explored map. This becomes challenging when the robot experiences positioning error, does not have an external positioning device, nor the luxury of engineered landmarks place ..."
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Cited by 187 (10 self)
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One of the critical components of mapping an unknown environment is the robot's ability to locate itself on a partially explored map. This becomes challenging when the robot experiences positioning error, does not have an external positioning device, nor the luxury of engineered landmarks placed in its free space. This paper presents a new method for simultaneous localization and mapping that exploits the topology of the robot's free space to localize the robot on a partially constructed map. The topology of the environment is encoded in a topological map; the particular topological map used in this paper is the generalized Voronoi graph (GVG), which also encodes some metric information about the robot's environment, as well. In this paper, we present the lowlevel control laws that generate the GVG edges and nodes, thereby allowing for exploration of an unknown space. With these prescribed control laws, the GVG (or other topological map) can be viewed as an arbitrator for a hybrid control system that determines when to invoke a particular lowlevel controller from a set of controllers all working toward the highlevel capability of mobile robot exploration. The main contribution, however, is using the graph structure of the GVG, via a graph matching process, to localize the robot. Experimental results verify the described work. Index TermsExploration, localization, mapping, mobile robots, motion planning, tologoical maps, Voronoi diagrams. I.
Probabilistic Mapping Of An Environment By A Mobile Robot
 In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA
, 1998
"... This paper addresses the problem of building largescale maps of indoor environments with mobile robots. It proposes a statistical approach that phrases the map building problem as a constrained maximumlikelihood estimation problem, for which it devises a practical algorithm. Experimental results i ..."
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Cited by 41 (3 self)
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This paper addresses the problem of building largescale maps of indoor environments with mobile robots. It proposes a statistical approach that phrases the map building problem as a constrained maximumlikelihood estimation problem, for which it devises a practical algorithm. Experimental results in large, cyclic environments illustrate the appropriateness of the approach. 1 Introduction The problem of acquiring maps in largescale indoor environments has received considerable attention in the mobile robotics community. The problem of map building is the problem determining the location of entitiesofinterest(such as: landmarks, obstacles) in a global frame of reference (such as a Cartesian coordinate frame). To build a map of its environment, a robot must know where it is. Since robot motion is inaccurate, the robot must solve a concurrent localization problem, whose difficulty increases with the size of the environment (and specifically with the size of possible cycles therein). T...
Towards Exact Localization without Explicit Localization with the Generalized Voronoi Graph
 In IEEE Int. Conf. on Robotics and Automation, Lueven
, 1998
"... . Sensor based exploration is a task which enables a robot to explore and map an unknown environment, using sensor information. The map used in this paper is the generalized Voronoi graph (GVG). The robot explores an unknown environment using an already developed incremental construction procedure t ..."
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Cited by 20 (4 self)
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. Sensor based exploration is a task which enables a robot to explore and map an unknown environment, using sensor information. The map used in this paper is the generalized Voronoi graph (GVG). The robot explores an unknown environment using an already developed incremental construction procedure to generate the GVG using sensor information. This paper presents some initial results which uses the GVG for robot localization, while mitigating the need to update encoder values. Experimental results verify the described work. 1 Introduction Sensor based exploration enables a robot to explore an unknown environment, and using its sensor information, build a map of that environment. A critical component to this task is the robot's ability to ascertain its location in the partially explored map or to determine that it has entered new territory. Many conventional methods attempt to make this determination via a localization scheme which updates the (x; y) coordinates of the robot. Most robo...
A probabilistic approach to concurrent map acquisition 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 da ..."
Abstract

Cited by 7 (1 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, along 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. y These authors are affiliated with the Institut für Informatik III, Universät Bonn, Germany
Concurrent object identification and localization for a mobile robot. Künstliche Intelligenz
, 2000
"... Identification and localization of taskrelevant objects is an essential problem for advanced service robots. We integrate stateoftheart techniques both for object identification and object localization to solve this problem. Based on a multilevel spatial representation architecture, our approach ..."
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Cited by 3 (1 self)
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Identification and localization of taskrelevant objects is an essential problem for advanced service robots. We integrate stateoftheart techniques both for object identification and object localization to solve this problem. Based on a multilevel spatial representation architecture, our approach integrates methods for mapping, selflocalization and spatial reasoning for navigation with visual attention, feature detection, and hierarchical neural classifiers. By combining probabilistic representations with qualitative spatial representations, the robot can robustly localize and navigate to previously detected objects, and also associate symbolic knowledge with taskrelevant objects, which is essential for task planning and interaction with humans. 1
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"... An efficient probabilistic algorithm for the concurrent mapping and localization problem that arises in mobile robotics is presented. The algorithm addresses the problem in which a team of robots builds a map online while simultaneously accommodating errors in the robots ’ odometry. At the core of ..."
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An efficient probabilistic algorithm for the concurrent mapping and localization problem that arises in mobile robotics is presented. The algorithm addresses the problem in which a team of robots builds a map online while simultaneously accommodating errors in the robots ’ odometry. 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 environments with cycles. The algorithm can be implemented in a distributed manner 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 three dimensions. KEY WORDS—mobile robotics, map acquisition, localization, robotic exploration, multirobot systems, threedimensional modeling 1.