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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 153 (35 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.
FrameSLAM: From bundle adjustment to realtime visual mapping
 IEEE Trans. on Robotics
, 2008
"... Abstract—Many successful indoor mapping techniques employ frametoframe matching of laser scans to produce detailed local maps as well as the closing of large loops. In this paper, we propose a framework for applying the same techniques to visual imagery. We match visual frames with large numbers o ..."
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Cited by 148 (7 self)
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Abstract—Many successful indoor mapping techniques employ frametoframe matching of laser scans to produce detailed local maps as well as the closing of large loops. In this paper, we propose a framework for applying the same techniques to visual imagery. We match visual frames with large numbers of point features, using classic bundle adjustment techniques from computational vision, but we keep only relative frame pose information (a skeleton). The skeleton is a reduced nonlinear system that is a faithful approximation of the larger system and can be used to solve large loop closures quickly, as well as forming a backbone for data association and local registration. We illustrate the workings of the system with large outdoor datasets (10 km), showing largescale loop closure and precise localization in real time. Index Terms—Visual mapping, visual odometry, visual SLAM. I.
Square Root SAM: Simultaneous localization and mapping via square root information smoothing
 International Journal of Robotics Reasearch
, 2006
"... Solving the SLAM problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. We investigate smoothing approaches as a viable alternative to extended Kalman filterbased solutions to the problem. In particular, we look at approaches that factorize either th ..."
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Cited by 144 (39 self)
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Solving the SLAM problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. We investigate smoothing approaches as a viable alternative to extended Kalman filterbased solutions to the problem. In particular, we look at approaches 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. In this paper we present the theory underlying these methods, along with an interpretation of factorization in terms of the graphical model associated with the SLAM problem. We present both simulation results and actual SLAM experiments in largescale environments that underscore the potential of these methods as an alternative to EKFbased approaches. 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 112 (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 102 (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.
Simultaneous Localisation and Mapping (SLAM): Part I The Essential Algorithms
 IEEE ROBOTICS AND AUTOMATION MAGAZINE
, 2006
"... This tutorial provides an introduction to Simultaneous Localisation and Mapping (SLAM) and the extensive research on SLAM that has been undertaken over the past decade. SLAM is the process by which a mobile robot can build a map of an environment and at the same time use this map to compute it’s own ..."
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Cited by 101 (2 self)
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This tutorial provides an introduction to Simultaneous Localisation and Mapping (SLAM) and the extensive research on SLAM that has been undertaken over the past decade. SLAM is the process by which a mobile robot can build a map of an environment and at the same time use this map to compute it’s own location. The past decade has seen rapid and exciting progress in solving the SLAM problem together with many compelling implementations of SLAM methods. Part I of this tutorial (this paper), describes the probabilistic form of the SLAM problem, essential solution methods and significant implementations. Part II of this tutorial will be concerned with recent advances in computational methods and new formulations of the SLAM problem for large scale and complex environments.
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.
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 83 (11 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.
Exactly sparse delayedstate filters
 in IEEE Intl. Conf. on Robotics and Automation (ICRA
, 2005
"... Abstract — This paper presents the novel insight that the SLAM information matrix is exactly sparse in a delayedstate framework. Such a framework is used in viewbased representations of the environment which rely upon scanmatching raw sensor data. Scanmatching raw data results in virtual observati ..."
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Cited by 76 (11 self)
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Abstract — This paper presents the novel insight that the SLAM information matrix is exactly sparse in a delayedstate framework. Such a framework is used in viewbased representations of the environment which rely upon scanmatching raw sensor data. Scanmatching raw data results in virtual observations of robot motion with respect to a place its previously been. The exact sparseness of the delayedstate information matrix is in contrast to other recent featurebased SLAM information algorithms like Sparse Extended Information Filters or Thin Junction Tree Filters. These methods have to make approximations in order to force the featurebased SLAM information matrix to be sparse. The benefit of the exact sparseness of the delayedstate framework is that it allows one to take advantage of the information space parameterization without having to make any approximations. Therefore, it can produce equivalent results to the “fullcovariance ” solution. Index Terms — Delayed states, EIF, SLAM. I.
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 74 (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.