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76
Using the topological skeleton for scalable global metrical mapbuilding
 In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems
, 2004
"... Abstract — Most simultaneous localization and mapping (SLAM) approaches focus on purely metrical approaches to mapbuilding. We present a method for computing the global metrical map that builds on the structure provided by a topological map. This allows us to factor the uncertainty in the map into ..."
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Cited by 37 (11 self)
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Abstract — Most simultaneous localization and mapping (SLAM) approaches focus on purely metrical approaches to mapbuilding. We present a method for computing the global metrical map that builds on the structure provided by a topological map. This allows us to factor the uncertainty in the map into local metrical uncertainty (which is handled well by existing SLAM methods), global topological uncertainty (which is handled well by recently developed topological maplearning methods), and global metrical uncertainty (which can be handled effectively once the other types of uncertainty are factored out). We believe that this method for building the global metrical map will be scalable to very large environments. I.
Bayesian inference in the space of topological maps
 IEEE Transactions on Robotics
, 2006
"... Abstract—While probabilistic techniques have previously been investigated extensively for performing inference over the space of metric maps, no corresponding generalpurpose methods exist for topological maps. We present the concept of probabilistic topological maps (PTMs), a samplebased represent ..."
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Cited by 37 (1 self)
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Abstract—While probabilistic techniques have previously been investigated extensively for performing inference over the space of metric maps, no corresponding generalpurpose methods exist for topological maps. We present the concept of probabilistic topological maps (PTMs), a samplebased representation that approximates the posterior distribution over topologies, given available sensor measurements. We show that the space of topologies is equivalent to the intractably large space of set partitions on the set of available measurements. The combinatorial nature of the problem is overcome by computing an approximate, samplebased representation of the posterior. The PTM is obtained by performing Bayesian inference over the space of all possible topologies, and provides a systematic solution to the problem of perceptual aliasing in the domain of topological mapping. In this paper, we describe a general framework for modeling measurements, and the use of a Markovchain Monte Carlo algorithm that uses specific instances of these models for odometry and appearance measurements to estimate the posterior distribution. We present experimental results that validate our technique and generate good maps when using odometry and appearance, derived from panoramic images, as sensor measurements. Index Terms—Bayesian inference, Markovchain Monte Carlo (MCMC), mobile robots, perceptual aliasing, probability distributions, samplebased representations, topological maps. I.
Hybrid simultaneous localization and map building: closing the loop with multihypothesis tracking
 In Proc. 2002 IEEE Intl. Conf. on Robotics & Automation
"... In this paper simultaneous localization and map building is performed with a hybrid, metric topological, approach. A global topological map connects local metric maps, allowing a compact environment model, which does not require global metric consistency and permits both precision and robustness. H ..."
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Cited by 34 (0 self)
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In this paper simultaneous localization and map building is performed with a hybrid, metric topological, approach. A global topological map connects local metric maps, allowing a compact environment model, which does not require global metric consistency and permits both precision and robustness. However, the most important innovation of the approach is the way how loops in the environment are handled by map building using the information of the multi hypotheses topological localization. The method uses data from a 360 ° laser scanner to extract corners and openings for the topological approach and infinite lines for the metric method. This hybrid approach has been tested in a 50 x 25 m 2 portion of the institute building with a fully autonomous robot. The performances of the whole system are proven empirically by comparing maps generated by independent explorations, testing the localization capabilities,
SonarBased Mapping With Mobile Robots Using EM
"... This paper presents an algorithms for learning occupancy grid maps with mobile robots equipped with range finders, such as sonar sensors. Our approach employs the EM algorithm to solve the concurrent mapping and localization problem. To accommodate the spatial nature of range data, it relies on a tw ..."
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Cited by 33 (2 self)
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This paper presents an algorithms for learning occupancy grid maps with mobile robots equipped with range finders, such as sonar sensors. Our approach employs the EM algorithm to solve the concurrent mapping and localization problem. To accommodate the spatial nature of range data, it relies on a twolayered representation of maps, where global maps are composed from a collection of small, local maps. To avoid local minima during likelihood maximization, a softmax version of the M step is proposed that is gradually annealed to the exact maximum. Experimental results demonstrate that our approach is well suited for constructing large maps of typical indoor environments using sensors as inaccurate as sonars.
Simultaneous localization and map building: A global topological model with local metric maps
 IN PROCEEDINGS OF THE 2001 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS
, 2001
"... In this paper an approach combining the metric and topological paradigm for simultaneous localization and map building is presented. The main idea is to connect local metric maps by means of a global topological map. This allows a compact environment model which does not require global metric consis ..."
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Cited by 28 (1 self)
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In this paper an approach combining the metric and topological paradigm for simultaneous localization and map building is presented. The main idea is to connect local metric maps by means of a global topological map. This allows a compact environment model which does not require global metric consistency and permits both precision and robustness. The method uses a 360 degree laser scanner in order to extract corners and openings for the topological approach and lines for the metric localization. The approach has been tested in a 30 x 25 m portion of the institute building with the fully autonomous robot Donald Duck. An experiment consists of a complete exploration and a set of test missions. Three experiments have been performed for a total of 15 test missions, which have been randomly defined and completed with a success ratio of 87%.
Factoring the Mapping Problem: Mobile Robot MapBuilding in the Hybrid Spatial Semantic Hierarchy
, 2008
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A Logical Account of Causal and Topological Maps
, 2001
"... The Spatial Semantic Hierarchy (SSH) is a set of distinct representations for large scale space, each with its own ontology and each abstracted from the levels below it. At the control level, the agent and its environment are modeled as continuous dynamical systems whose equilibrium points are abstr ..."
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Cited by 18 (3 self)
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The Spatial Semantic Hierarchy (SSH) is a set of distinct representations for large scale space, each with its own ontology and each abstracted from the levels below it. At the control level, the agent and its environment are modeled as continuous dynamical systems whose equilibrium points are abstracted to a discrete set of distinctive states. The control laws whose execution defines trajectories linking these states are abstracted to actions, giving a discrete causal graph representation for the state space. The causal graph of states and actions is in turn abstracted to a topological network of places and paths (i.e. the topological map). Local metrical models of places and paths can be built within the framework of the control, causal and topological levels while avoiding problems of global consistency. ...
Building globally consistent gridmaps from topologies
 in Proc. of the 6th Int. IFAC Symp. on Robot Control (SYRORO00
, 2000
"... Abstract: This paper addresses the problem of recovering metric consistency in a global gridmap for mobile robot navigation in largescale environments. A hierarchy of robot maps is proposed which integrates topological and gridbased representations at dierent levels of abstraction. The consistency ..."
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Cited by 16 (3 self)
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Abstract: This paper addresses the problem of recovering metric consistency in a global gridmap for mobile robot navigation in largescale environments. A hierarchy of robot maps is proposed which integrates topological and gridbased representations at dierent levels of abstraction. The consistency problem is solved at the topological level, by applying a relaxation technique to generate coordinates for the places in the map. Consequently, the robot is able to recover a globally consistent gridmap without requiring accurate sensors or high computational costs. Experiments on a Nomad 200 robot in a large, real world environment demonstrate the eectiveness of the approach. Copyright c
2000 IFAC
Mobile Robot Relocation from Echolocation Constraints
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 2000
"... This paper presents a method for relocation of a mobile robot using sonar data. The process of determining the pose of a mobile robot with respect to a global reference frame in situations where no a priori estimate of the robot's location is available is cast as a problem of searching for corr ..."
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Cited by 15 (2 self)
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This paper presents a method for relocation of a mobile robot using sonar data. The process of determining the pose of a mobile robot with respect to a global reference frame in situations where no a priori estimate of the robot's location is available is cast as a problem of searching for correspondences between measurements and an a priori map of the environment. A physicallybased sonar sensor model is used to characterize the geometric constraints provided by echolocation measurements of different types of objects. Individual range returns are used as data features in a constraintbased search to determine the robot's position. A hypothesize and test technique is employed in which positions of the robot are calculated from all possible combinations of two range returns that satisfy the measurement model. The algorithm determines the positions which provide the best match between the range returns and the environment model. The performance of the approach is demonstrated using data from both a single scanning Polaroid sonar and from a ring of Polaroid sonar sensors.
Inference in the Space of Topological Maps: An MCMCbased Approach
"... While probabilistic techniques have been considered extensively in the context of metric maps, no general purpose probabilistic methods exist for topological maps. We present the concept of Probabilistic Topological Maps (PTMs), a samplebased representation that approximates the posterior distributi ..."
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Cited by 13 (2 self)
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While probabilistic techniques have been considered extensively in the context of metric maps, no general purpose probabilistic methods exist for topological maps. We present the concept of Probabilistic Topological Maps (PTMs), a samplebased representation that approximates the posterior distribution over topologies given the available sensor measurements. The PTM is obtained through the use of MCMCbased Bayesian inference over the space of all possible topologies. It is shown that the space of all topologies is equivalent to the space of set partitions of all available measurements. While the space of possible topologies is intractably large, our use of Markov chain Monte Carlo sampling to infer the approximate histograms overcomes the combinatorial nature of this space and provides a general solution to the correspondence problem in the context of topological mapping. We present experimental results that validate our technique and generate good maps even when using only odometry as the sensor measurements.