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FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem
 In Proceedings of the AAAI National Conference on Artificial Intelligence
, 2002
"... The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filterbase ..."
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Cited by 588 (10 self)
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The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filterbased algorithms, for example, require time quadratic in the number of landmarks to incorporate each sensor observation. This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map. This algorithm is based on a factorization of the posterior into a product of conditional landmark distributions and a distribution over robot paths. The algorithm has been run successfully on as many as 50,000 landmarks, environments far beyond the reach of previous approaches. Experimental results demonstrate the advantages and limitations of the FastSLAM algorithm on both simulated and realworld data.
FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges
"... In [15], Montemerlo et al. proposed an algorithm called FastSLAM as an efficient and robust solution to the simultaneous localization and mapping problem. This paper describes a modified version of FastSLAM that overcomes important deficiencies of the original algorithm. We prove convergence of this ..."
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Cited by 227 (7 self)
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In [15], Montemerlo et al. proposed an algorithm called FastSLAM as an efficient and robust solution to the simultaneous localization and mapping problem. This paper describes a modified version of FastSLAM that overcomes important deficiencies of the original algorithm. We prove convergence of this new algorithm for linear SLAM problems and provide realworld experimental results that illustrate an order of magnitude improvement in accuracy over the original FastSLAM algorithm. 1
The GraphSLAM algorithm with applications to largescale mapping of urban structures
 INTERNATIONAL JOURNAL ON ROBOTICS RESEARCH
, 2006
"... This article presents GraphSLAM, a unifying algorithm for the offline SLAM problem. GraphSLAM is closely related to a recent sequence of research papers on applying optimization techniques to SLAM problems. It transforms the SLAM posterior into a graphical network, representing the loglikelihood of ..."
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Cited by 100 (2 self)
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This article presents GraphSLAM, a unifying algorithm for the offline SLAM problem. GraphSLAM is closely related to a recent sequence of research papers on applying optimization techniques to SLAM problems. It transforms the SLAM posterior into a graphical network, representing the loglikelihood of the data. It then reduces this graph using variable elimination techniques, arriving at a lowerdimensional problems that is then solved using conventional optimization techniques. As a result, GraphSLAM can generate maps with 10 8 or more features. The paper discusses a greedy algorithm for data association, and presents results for SLAM in urban environments with occasional GPS measurements.
A System for Volumetric Robotic Mapping of Abandoned Mines
 IN PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA
, 2003
"... This paper describes two robotic systems developed for acquiring accurate volumetric maps of underground mines. One system is based on a cart instrumented by laser range finders, pushed through a mine by people. Another is a remotely controlled mobile robot equipped with laser range finders. To buil ..."
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Cited by 77 (21 self)
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This paper describes two robotic systems developed for acquiring accurate volumetric maps of underground mines. One system is based on a cart instrumented by laser range finders, pushed through a mine by people. Another is a remotely controlled mobile robot equipped with laser range finders. To build consistent maps of large mines with many cycles, we describe an algorithm for estimating global correspondences and aligning robot paths. This algorithm enables us to recover consistent maps several hundreds of meters in diameter, without odometric information. We report results obtained in two mines, a research mine in Bruceton, PA, and an abandoned coal mine in Burgettstown, PA.
Consistency of the EKFSLAM algorithm
 In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
, 2006
"... Abstract — This paper presents an analysis of the extended Kalman filter formulation of simultaneous localisation and mapping (EKFSLAM). We show that the algorithm produces very optimistic estimates once the “true ” uncertainty in vehicle heading exceeds a limit. This failure is subtle and cannot, ..."
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Cited by 70 (1 self)
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Abstract — This paper presents an analysis of the extended Kalman filter formulation of simultaneous localisation and mapping (EKFSLAM). We show that the algorithm produces very optimistic estimates once the “true ” uncertainty in vehicle heading exceeds a limit. This failure is subtle and cannot, in general, be detected without groundtruth, although a very inconsistent filter may exhibit observable symptoms, such as disproportionately large jumps in the vehicle pose update. Conventional solutions—adding stabilising noise, using an iterated EKF or unscented filter, etc—do not improve the situation. However, if “small ” heading uncertainty is maintained, EKFSLAM exhibits consistent behaviour over an extended timeperiod. Although the uncertainty estimate slowly becomes optimistic, inconsistency can be mitigated indefinitely by applying tactics such as batch updates or stabilising noise. The manageable degradation of small heading variance SLAM indicates the efficacy of submap methods for largescale maps. I.
6D SLAM with an Application in Autonomous Mine Mapping
 In Proceedings of the IEEE International Conference on Robotics and Automation
, 2004
"... To create with an autonomous mobile robot a 3D volumetric map of a scene it is necessary to gage several 3D scans and to merge them into one consistent 3D model. This paper provides a new solution to the simultaneous localization and mapping (SLAM) problem with six degrees of freedom. Robot motion o ..."
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Cited by 55 (14 self)
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To create with an autonomous mobile robot a 3D volumetric map of a scene it is necessary to gage several 3D scans and to merge them into one consistent 3D model. This paper provides a new solution to the simultaneous localization and mapping (SLAM) problem with six degrees of freedom. Robot motion on natural surfaces has to cope with yaw, pitch and roll angles, turning pose estimation into a problem in six mathematical dimensions. A fast variant of the Iterative Closest Points algorithm registers the 3D scans in a common coordinate system and relocalizes the robot. Finally, consistent 3D maps are generated using a global relaxation. The algorithms have been tested with 3D scans taken in the Mathies mine, Pittsburgh, PA. Abandoned mines pose significant problems to society, yet a large fraction of them lack accurate 3D maps.
FastSLAM: An efficient solution to the simultaneous localization and mapping problem with unknown data association
 Journal of Machine Learning Research
"... This article provides a comprehensive description of FastSLAM, a new family of algorithms for the simultaneous localization and mapping problem, which specifically address hard data association problems. The algorithm uses a particle filter for sampling robot paths, and extended Kalman filters for r ..."
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Cited by 41 (0 self)
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This article provides a comprehensive description of FastSLAM, a new family of algorithms for the simultaneous localization and mapping problem, which specifically address hard data association problems. The algorithm uses a particle filter for sampling robot paths, and extended Kalman filters for representing maps acquired by the vehicle. This article presents two variants of this algorithm, the original algorithm along with a more recent variant that provides improved performance in certain operating regimes. In addition to a mathematical derivation of the new algorithm, we present a proof of convergence and experimental results on its performance on realworld data. 1
Monocular SLAM as a Graph of Coalesced Observations
"... We present a monocular SLAM system that avoids inconsistency by coalescing observations into independent local coordinate frames, building a graph of the local frames, and optimizing the resulting graph. We choose coordinates that minimize the nonlinearity of the updates in the nodes, and suggest a ..."
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Cited by 35 (1 self)
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We present a monocular SLAM system that avoids inconsistency by coalescing observations into independent local coordinate frames, building a graph of the local frames, and optimizing the resulting graph. We choose coordinates that minimize the nonlinearity of the updates in the nodes, and suggest a heuristic measure of such nonlinearity, using it to guide our traversal of the graph. The system operates in realtime on sequences with several hundreds of landmarks while performing global graph optimization, yielding accurate and nearly consistent estimation relative to offline bundle adjustment, and considerably better consistency than EKF SLAM and FastSLAM. 1.
Inference on Networks of Mixtures for Robust Robot Mapping
, 2013
"... The central challenge in robotic mapping is obtaining reliable data associations (or “loop closures”): stateoftheart inference algorithms can fail catastrophically if even one erroneous loop closure is incorporated into the map. Consequently, much work has been done to push error rates closer to ..."
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Cited by 19 (3 self)
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The central challenge in robotic mapping is obtaining reliable data associations (or “loop closures”): stateoftheart inference algorithms can fail catastrophically if even one erroneous loop closure is incorporated into the map. Consequently, much work has been done to push error rates closer to zero. However, a longlived or multirobot system will still encounter errors, leading to system failure. We propose a fundamentally different approach: allow richer error models that allow the probability of a failure to be explicitly modeled. In other words, rather than characterizing loop closures as being “right ” or “wrong”, we propose characterizing the error of those loop closures in a more expressive manner that can account for their nonGaussian behavior. Our approach leads to an fullyintegrated Bayesian framework for dealing with errorprone data. Unlike earlier multiplehypothesis approaches, our approach avoids exponential memory complexity and is fast enough for realtime performance. We show that the proposed method not only allows loop closing errors to be automatically identified, but also that in extreme cases, the “frontend ” loopvalidation systems can be unnecessary. We demonstrate our system both on standard benchmarks and on the realworld datasets that motivated this work. 1
Which Landmark is Useful? Learning Selection Policies for Navigation in Unknown Environments
"... Abstract — In general, a mobile robot that operates in unknown environments has to maintain a map and has to determine its own location given the map. This introduces significant computational and memory constraints for most autonomous systems, especially for lightweight robots such as humanoids or ..."
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Cited by 18 (10 self)
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Abstract — In general, a mobile robot that operates in unknown environments has to maintain a map and has to determine its own location given the map. This introduces significant computational and memory constraints for most autonomous systems, especially for lightweight robots such as humanoids or flying vehicles. In this paper, we present a novel approach for learning a landmark selection policy that allows a robot to discard landmarks that are not valuable for its current navigation task. This enables the robot to reduce the computational burden and to carry out its task more efficiently by maintaining only the important landmarks. Our approach applies an unscented Kalman filter for addressing the simultaneous localization and mapping problems and uses MonteCarlo reinforcement learning to obtain the selection policy. Based on real world and simulation experiments, we show that the learned policies allow for efficient robot navigation and outperform handcrafted strategies. We furthermore demonstrate that the learned policies are not only usable in a specific scenario but can also be generalized towards environments with varying properties. I.