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128
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 225 (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
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 179 (20 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
Square Root SAM: Simultaneous localization and mapping via square root information smoothing
, 2006
"... Solving the SLAM (simultaneous localization and mapping) problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. Smoothing approaches have been investigated as a viable alternative to extended Kalman filter (EKF)based solutions to the problem. In parti ..."
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Cited by 144 (38 self)
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Solving the SLAM (simultaneous localization and mapping) problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. Smoothing approaches have been investigated as a viable alternative to extended Kalman filter (EKF)based solutions to the problem. In particular, approaches have been looked at that factorize either the associated information matrix or the measurement Jacobian into square root form. Such techniques have several significant advantages over the EKF: they are faster yet exact; they can be used in either batch or incremental mode; are better equipped to deal with nonlinear process and measurement models; and yield the entire robot trajectory, at lower cost for a large class of SLAM problems. In addition, in an indirect but dramatic way, column ordering heuristics automatically exploit the locality inherent in the geographic nature of the SLAM problem. This paper presents the theory underlying these methods, along with an interpretation of factorization in terms of the graphical model associated with the SLAM problem. Both simulation results and actual SLAM experiments in largescale environments are presented that underscore the potential of these methods as an alternative to EKFbased approaches.
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 117 (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.
A Multilevel Relaxation Algorithm for Simultaneous Localisation and Mapping
, 2004
"... This paper addresses the problem of simultaneous localisation and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation meth ..."
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Cited by 110 (5 self)
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This paper addresses the problem of simultaneous localisation and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling nonlinearities compared to other SLAM algorithms. Experimental comparisons with alternative algorithms using two wellknown data sets and mapping results on a real robot are also presented.
Exactly sparse delayedstate filters for viewbased SLAM
 IEEE Transactions on Robotics
, 2006
"... Abstract—This paper reports the novel insight that the simultaneous localization and mapping (SLAM) information matrix is exactly sparse in a delayedstate framework. Such a framework is used in viewbased representations of the environment that rely upon scanmatching raw sensor data to obtain virt ..."
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Cited by 105 (21 self)
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Abstract—This paper reports the novel insight that the simultaneous localization and mapping (SLAM) information matrix is exactly sparse in a delayedstate framework. Such a framework is used in viewbased representations of the environment that rely upon scanmatching raw sensor data to obtain virtual observations of robot motion with respect to a place it has previously been. The exact sparseness of the delayedstate information matrix is in contrast to other recent featurebased SLAM information algorithms, such as sparse extended information filter or thin junctiontree filter, since these methods have to make approximations in order to force the featurebased SLAM information matrix to be sparse. The benefit of the exact sparsity of the delayedstate framework is that it allows one to take advantage of the information space parameterization without incurring any sparse approximation error. Therefore, it can produce equivalent results to the fullcovariance solution. The approach is validated experimentally using monocular imagery for two datasets: a testtank experiment with ground truth, and a remotely operated vehicle survey of the RMS Titanic. Index Terms—Information filters, Kalman filtering, machine vision, mobile robot motion planning, mobile robots, recursive estimation, robot vision systems, simultaneous localization and mapping (SLAM), underwater vehicles. I.
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 104 (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.
Fast iterative alignment of pose graphs with poor initial estimates
 In IEEE Intl. Conf. on Robotics and Automation (ICRA
, 2006
"... Abstract — A robot exploring an environment can estimate its own motion and the relative positions of features in the environment. Simultaneous Localization and Mapping (SLAM) algorithms attempt to fuse these estimates to produce a map and a robot trajectory. The constraints are generally nonlinear ..."
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Cited by 81 (9 self)
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Abstract — A robot exploring an environment can estimate its own motion and the relative positions of features in the environment. Simultaneous Localization and Mapping (SLAM) algorithms attempt to fuse these estimates to produce a map and a robot trajectory. The constraints are generally nonlinear, thus SLAM can be viewed as a nonlinear optimization problem. The optimization can be difficult, due to poor initial estimates arising from odometry data, and due to the size of the state space. We present a fast nonlinear optimization algorithm that rapidly recovers the robot trajectory, even when given a poor initial estimate. Our approach uses a variant of Stochastic Gradient Descent on an alternative statespace representation that has good stability and computational properties. We compare our algorithm to several others, using both real and synthetic data sets.
Visually navigating the RMS Titanic with SLAM information filters
 in Proceedings of Robotics: Science and Systems
, 2005
"... Abstract — This paper describes a visionbased, largearea, simultaneous localization and mapping (SLAM) algorithm that respects the lowoverlap imagery constraints typical of underwater vehicles while exploiting the inertial sensor information that is routinely available on such platforms. We prese ..."
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Cited by 75 (12 self)
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Abstract — This paper describes a visionbased, largearea, simultaneous localization and mapping (SLAM) algorithm that respects the lowoverlap imagery constraints typical of underwater vehicles while exploiting the inertial sensor information that is routinely available on such platforms. We present a novel strategy for efficiently accessing and maintaining consistent covariance bounds within a SLAM information filter, thereby greatly increasing the reliability of data association. The technique is based upon solving a sparse system of linear equations coupled with the application of constanttime Kalman updates. The method is shown to produce consistent covariance estimates suitable for robot planning and data association. Realworld results are presented for a visionbased 6DOF SLAM implementation using data from a recent ROV survey of the wreck of the RMS Titanic. I.