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37
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.
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.
Relaxation on a Mesh: a Formalism for Generalized Localization
, 2001
"... This paper considers two problems which at first sight appear to be quite distinct: localizing a robot in an unknown environment and calibrating an embedded sensor network. We show that both of these can be formulated as special cases of a generalized localization problem. In the standard localizati ..."
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Cited by 75 (8 self)
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This paper considers two problems which at first sight appear to be quite distinct: localizing a robot in an unknown environment and calibrating an embedded sensor network. We show that both of these can be formulated as special cases of a generalized localization problem. In the standard localization problem, the aim is to determine the pose of some object (usually a mobile robot) relative to a global coordinate system. In our generalized version, the aim is to determine the pose of all elements in a network (both fixed and mobile) relative to an arbitrary global coordinate system. We have developed a physically inspired `meshbased ' formalism for solving such problems. This paper outlines the formalism, and describes its application to the concrete tasks of multirobot mapping and calibration of a distributed sensor network. The paper presents experimental results for both tasks obtained using a set of Pioneer mobile robots equipped with scanning laser rangefinders.
Learning globally consistent maps by relaxation
 In Proceedings of the IEEE International Conference on Robotics and Automation
, 2000
"... Mobile robots require the ability to build their own maps to operate in unknown environments. A fundamental problem is that odometrybased dead reckoning cannot be used to assign accurate global position information to a map because of drift errors caused by wheel slippage. This paper introduces a ..."
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Cited by 71 (4 self)
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Mobile robots require the ability to build their own maps to operate in unknown environments. A fundamental problem is that odometrybased dead reckoning cannot be used to assign accurate global position information to a map because of drift errors caused by wheel slippage. This paper introduces a fast, online method of learning globally consistent maps, using only local metric information. The approach differs from previous work in that it is computationally cheap, easy to implement and is guaranteed to find a globally optimal solution. Experiments are presented in which large, complex environments were successfully mapped by a real robot, and quantitative performance measures are used to assess the quality of the maps obtained. 1
Graphical SLAM  a selfcorrecting map
 In IEEE International Conference on Robotics and Automation
, 2004
"... AbstractIn 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 constrai ..."
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Cited by 60 (4 self)
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AbstractIn 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.
Localization for Mobile Robot Teams Using Maximum Likelihood Estimation
 In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
, 2002
"... This paper describes a method for localizing the members of a mobile robot team, using only the robots themselves as landmarks. That is, we describe a method whereby each robot can determine the relative range, bearing and orientation of every other robot in the team, without the use of GPS, externa ..."
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Cited by 58 (1 self)
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This paper describes a method for localizing the members of a mobile robot team, using only the robots themselves as landmarks. That is, we describe a method whereby each robot can determine the relative range, bearing and orientation of every other robot in the team, without the use of GPS, external landmarks, or instrumentation of the environment. Our method assumes that each robot is able to measure the relative pose of nearby robots, together with changes in its own pose; using a combination of maximum likelihood estimation (MLE) and numerical optimization, we can subsequently infer the relative pose of every robot in the team. This paper describes the basic formalism, its practical implementation, and presents experimental results obtained using a team of four mobile robots.
Simultaneous Localization and Mapping  A Discussion
, 2001
"... This papers provides two contributions to the problem of Simultaneous Localization and Mapping (SLAM): First we discuss properties of the problem itself and of the intended semantics of an uncertain map representation, with the main idea of "representing certainty of relations despite the uncer ..."
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Cited by 39 (7 self)
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This papers provides two contributions to the problem of Simultaneous Localization and Mapping (SLAM): First we discuss properties of the problem itself and of the intended semantics of an uncertain map representation, with the main idea of "representing certainty of relations despite the uncertainty of positions". We propose some requirements an ideal solution of SLAM should have concerning uncertainty, memory space and computation time and discuss existing approaches in the light of these requirements. The second part proposes a representation based on sparse information matrices together with some properties that motivate this approach. This is shown to comply to the uncertainty and space requirements. To derive an estimated map from the representation a sparse linear equation system has to be solved. However, an update of the representation itself needs only constant time, making it highly attractive for building a SLAM algorithm.
Using clustering information for sensor network localization
 in Proceedings of IEEE Conference on Distributed Computing in Sensor Systems (DCOSS 2005
, 2005
"... 0.1 Introduction Many wireless sensor network applications require information about the geographiclocation of each sensor node. Besides the typical application of correlating sensor readings with physical locations, approximate geographical localization is also neededfor applications such as locati ..."
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Cited by 35 (0 self)
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0.1 Introduction Many wireless sensor network applications require information about the geographiclocation of each sensor node. Besides the typical application of correlating sensor readings with physical locations, approximate geographical localization is also neededfor applications such as locationaided routing [2], geographic routing [3], geographic routing with imprecise geographic coordinates [4, 5], geographic hash tables [6], andfor many data aggregation applications.
HeuristicBased Laser Scan Matching for Outdoor 6D SLAM
 In Advances in artificial intelligence. 28th annual German Conf. on AI
, 2005
"... Abstract. 6D SLAM (Simultaneous Localization and Mapping) or 6D Concurrent Localization and Mapping of mobile robots considers six dimensions for the robot pose, namely, the x, y and z coordinates and the roll, yaw and pitch angles. Robot motion and localization on natural surfaces, e.g., driving wi ..."
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Cited by 27 (5 self)
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Abstract. 6D SLAM (Simultaneous Localization and Mapping) or 6D Concurrent Localization and Mapping of mobile robots considers six dimensions for the robot pose, namely, the x, y and z coordinates and the roll, yaw and pitch angles. Robot motion and localization on natural surfaces, e.g., driving with a mobile robot outdoor, must regard these degrees of freedom. This paper presents a robotic mapping method based on locally consistent 3D laser range scans. Scan matching, combined with a heuristic for closed loop detection and a global relaxation method, results in a highly precise mapping system for outdoor environments. The mobile robot Kurt3D was used to acquire data of the Schloss Birlinghoven campus. The resulting 3D map is compared with ground truth, given by an aerial photograph. 1
Probabilistic models of deadreckoning error in nonholonomic mobile robots
 In Proc. IEEE Int. Conf. Robotics and Automation (ICRA
, 2003
"... Abstract − In this paper, deadreckoning error in mobile robots is studied in the context of several different models. These models are derived first in the form of stochastic differential equations (SDEs). Corresponding FokkerPlanck equations are derived, and desired probability density functions ..."
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Cited by 21 (4 self)
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Abstract − In this paper, deadreckoning error in mobile robots is studied in the context of several different models. These models are derived first in the form of stochastic differential equations (SDEs). Corresponding FokkerPlanck equations are derived, and desired probability density functions (PDFs) of robot pose are computed by using the Fourier transform for SE(2). I.