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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 169 (8 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
Thin Junction Tree Filters for Simultaneous Localization and Mapping
 In Intl. Joint Conf. on Artificial Intelligence (IJCAI
, 2003
"... Simultaneous Localization and Mapping (SLAM) is a fundamental problem in mobile robotics: while a robot navigates in an unknown environment, it must incrementally build a map of its surroundings and localize itself within that map. Traditional approaches to the problem are based upon Kalman filters, ..."
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Cited by 124 (1 self)
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Simultaneous Localization and Mapping (SLAM) is a fundamental problem in mobile robotics: while a robot navigates in an unknown environment, it must incrementally build a map of its surroundings and localize itself within that map. Traditional approaches to the problem are based upon Kalman filters, but suffer from complexity issues: the size of the belief state and the time complexity of the filtering operation grow quadratically in the size of the map. This paper presents a filtering technique that maintains a tractable approximation of the filtered belief state as a thin junction tree. The junction tree grows under measurement and motion updates and is periodically "thinned" to remain tractable via efficient maximum likelihood projections. When applied to the SLAM problem, these thin junction tree filters have a linearspace belief state representation, and use a lineartime filtering operation. Further approximation can yield a constanttime filtering operation, at the expense of delaying the incorporation of observations into the majority of the map. Experiments on a suite of SLAM problems validate the approach.
Improving gridbased slam with raoblackwellized particle filters by adaptive proposals and selective resampling
 In Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA
, 2005
"... Abstract — Recently RaoBlackwellized particle filters have been introduced as effective means to solve the simultaneous localization and mapping (SLAM) problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is h ..."
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Cited by 116 (20 self)
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Abstract — Recently RaoBlackwellized particle filters have been introduced as effective means to solve the simultaneous localization and mapping (SLAM) problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is how to reduce the number of particles. In this paper we present adaptive techniques to reduce the number of particles in a RaoBlackwellized particle filter for learning grid maps. We propose an approach to compute an accurate proposal distribution taking into account not only the movement of the robot but also the most recent observation. This drastically decrease the uncertainty about the robot’s pose in the prediction step of the filter. Furthermore, we present an approach to selectively carry out resampling operations which seriously reduces the problem of particle depletion. Experimental results carried out with mobile robots in largescale indoor as well as in outdoor environments illustrate the advantages of our methods over previous approaches. I.
Improved techniques for grid mapping with raoblackwellized particle filters
 IEEE Transactions on Robotics
, 2007
"... Abstract — Recently, RaoBlackwellized particle filters have been introduced as an effective means to solve the simultaneous localization and mapping problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is how ..."
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Cited by 104 (19 self)
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Abstract — Recently, RaoBlackwellized particle filters have been introduced as an effective means to solve the simultaneous localization and mapping problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is how to reduce the number of particles. In this paper, we present adaptive techniques for reducing this number in a RaoBlackwellized particle filter for learning grid maps. We propose an approach to compute an accurate proposal distribution taking into account not only the movement of the robot but also the most recent observation. This drastically decreases the uncertainty about the robot’s pose in the prediction step of the filter. Furthermore, we present an approach to selectively carry out resampling operations which seriously reduces the problem of particle depletion. Experimental results carried out with real mobile robots in largescale indoor as well as in outdoor environments illustrate the advantages of our methods over previous approaches. Index Terms — SLAM, RaoBlackwellized particle filter, adaptive resampling, motionmodel, improved proposal
An Efficient FastSLAM Algorithm for Generating Maps of LargeScale Cyclic . . .
 IN PROC. OF THE IEEE/RSJ INT. CONF. ON INTELLIGENT ROBOTS AND SYSTEMS (IROS
, 2003
"... The ability to learn a consistent model of its environment is a prerequisite for autonomous mobile robots. A particularly challenging problem in acquiring environment maps is that of closing loops; loops in the environment create challenging data association problems [9]. This paper presents a novel ..."
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Cited by 91 (19 self)
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The ability to learn a consistent model of its environment is a prerequisite for autonomous mobile robots. A particularly challenging problem in acquiring environment maps is that of closing loops; loops in the environment create challenging data association problems [9]. This paper presents a novel algorithm that combines RaoBlackwellized particle filtering and scan matching. In our approach scan matching is used for minimizing odometric errors during mapping. A probabilistic model of the residual errors of scan matching process is then used for the resampling steps. This way the number of samples required is seriously reduced. Simultaneously we reduce the particle depletion problem that typically prevents the robot from closing large loops. We present extensive experiments that illustrate the superior performance of our approach compared to previous approaches.
iSAM: Incremental Smoothing and Mapping
, 2008
"... We present incremental smoothing and mapping (iSAM), a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing informatio ..."
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Cited by 88 (24 self)
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We present incremental smoothing and mapping (iSAM), a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, therefore recalculating only the matrix entries that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fillin in the factor matrix by periodic variable reordering. Also, to enable data association in realtime, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and realworld datasets for both landmark and poseonly settings.
Scalable monocular SLAM
 in IEEE Computer Society Conference on Computer Vision and Pattern Recognition
, 2006
"... Localization and mapping in unknown environments becomes more difficult as the complexity of the environment increases. With conventional techniques, the cost of maintaining estimates rises rapidly with the number of landmarks mapped. We present a monocular SLAM system that employs a particle filter ..."
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Cited by 88 (3 self)
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Localization and mapping in unknown environments becomes more difficult as the complexity of the environment increases. With conventional techniques, the cost of maintaining estimates rises rapidly with the number of landmarks mapped. We present a monocular SLAM system that employs a particle filter and topdown search to allow realtime performance while mapping large numbers of landmarks. To our knowledge, we are the first to apply this FastSLAMtype particle filter to singlecamera SLAM. We also introduce a novel partial initialization procedure that efficiently determines the depth of new landmarks. Moreover, we use information available in observations of new landmarks to improve camera pose estimates. Results show the system operating in realtime on a standard workstation while mapping hundreds of landmarks. 1.
Nonlinear constraint network optimization for efficient map learning
 IEEE Transactions on Intelligent Transportation Systems
"... Abstract — Learning models of the environment is one of the fundamental tasks of mobile robots since maps are needed for a wide range of robotic applications, such as navigation and transportation tasks, service robotic applications, and several others. In the past, numerous efficient approaches to ..."
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Cited by 51 (24 self)
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Abstract — Learning models of the environment is one of the fundamental tasks of mobile robots since maps are needed for a wide range of robotic applications, such as navigation and transportation tasks, service robotic applications, and several others. In the past, numerous efficient approaches to map learning have been proposed. Most of them, however, assume that the robot lives on a plane. In this paper, we present a highly efficient maximum likelihood approach that is able to solve 3D as well as 2D problems. Our approach addresses the socalled graphbased formulation of the simultaneous localization and mapping (SLAM) and can be seen as an extension of Olson’s algorithm [27] towards nonflat environments. It applies a novel parameterization of the nodes of the graph that significantly improves the performance of the algorithm and can cope with arbitrary network topologies. The latter allows us to bound the complexity of the algorithm to the size of the mapped area and not to the length of the trajectory. Furthermore, our approach is able to appropriately distribute the roll, pitch and yaw error over a sequence of poses in 3D mapping problems. We implemented our technique and compared it to multiple other graphbased SLAM solutions. As we demonstrate in simulated and in real world experiments, our method converges faster than the other approaches and yields accurate maps of the environment. I.
DPSLAM: Fast, robust simultaneous localization and mapping without predetermined landmarks
"... We present a novel, laser range finder based algorithm for simultaneous localization and mapping (SLAM) for mobile robots. SLAM addresses the problem of constructing an accurate map in real time despite imperfect information about the robot's trajectory through the environment. Unlike other app ..."
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Cited by 51 (0 self)
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We present a novel, laser range finder based algorithm for simultaneous localization and mapping (SLAM) for mobile robots. SLAM addresses the problem of constructing an accurate map in real time despite imperfect information about the robot's trajectory through the environment. Unlike other approaches that assume predetermined landmarks (and must deal with a resulting dataassociation problem) our algorithm is purely laser based. Our algorithm uses a particle filter to represent both robot poses and possible map configurations. By using a new map representation, which we call distributed particle (DP) mapping, we are able to maintain and update hundreds of candidate maps and robot poses efficiently. The worstcase complexity of our algorithm per laser sweep is logquadratic in the number of particles we maintain and linear in the area swept out by the laser. However, in practice our run time is usually much less than that. Our technique contains essentially no assumptions about the environment yet it is accurate enough to close loops of 60m in length with crisp, perpendicular edges on corridors and minimal or no misalignment errors. 1
H.: Graphical SLAM  a selfcorrecting map
 In: IEEE Intl. Conf. on Robotics and Automation (ICRA
, 2004
"... Abstract — In this paper we describe an approach to simultaneous localization and mapping, SLAM. This approach has the highly desirable property of robustness to data association errors. Another important advantage of our algorithm is that nonlinearities are computed exactly, so that global constra ..."
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Cited by 50 (2 self)
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Abstract — In this paper we describe an approach to simultaneous localization and mapping, SLAM. This approach has the highly desirable property of robustness to data association errors. Another important advantage of our algorithm is that nonlinearities are computed exactly, so that global constraints can be imposed even if they result in large shifts to the map. We represent the map as a graph and use the graph to find an efficient map update algorithm. We also show how topological consistency can be imposed on the map, such as, closing a loop. The algorithm has been implemented on an outdoor robot and we have experimental validation of our ideas. We also explain how the graph can be simplified leading to linear approximations of sections of the map. This reduction gives us a natural way to connect local map patches into a much larger global map. I.