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A solution to the simultaneous localization and map building (SLAM) problem
 IEEE Transactions on Robotics and Automation
, 2001
"... Abstract—The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle ..."
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Cited by 348 (28 self)
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Abstract—The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from the estimationtheoretic foundations of this problem developed in [1]–[3], this paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed. It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty. Together, these results show that it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and to compute simultaneously a bounded estimate of vehicle location. This paper also describes a substantial implementation of the SLAM algorithm on a vehicle operating in an outdoor environment using millimeterwave (MMW) radar to provide relative map observations. This implementation is used to demonstrate how some key issues such as map management and data association can be handled in a practical environment. The results obtained are crosscompared with absolute locations of the map landmarks obtained by surveying. In conclusion, this paper discusses a number of key issues raised by the solution to the SLAM problem including suboptimal mapbuilding algorithms and map management. Index Terms—Autonomous navigation, millimeter wave radar, simultaneous localization and map building. I.
MonoSLAM: Realtime single camera SLAM
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 2007
"... Abstract—We present a realtime algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the “pure vision ” domain of ..."
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Cited by 264 (21 self)
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Abstract—We present a realtime algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the “pure vision ” domain of a single uncontrolled camera, achieving real time but driftfree performance inaccessible to Structure from Motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We present applications of MonoSLAM to realtime 3D localization and mapping for a highperformance fullsize humanoid robot and live augmented reality with a handheld camera. Index Terms—Autonomous vehicles, 3D/stereo scene analysis, tracking. 1
Data Association in Stochastic Mapping Using the Joint Compatibility Test
, 2001
"... In this paper, we address the problem of robust data association for simultaneous vehicle localization and map building. We show that the classical gated nearest neighbor approach, which considers each matching between sensor observations and features independently, ignores the fact that measurement ..."
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Cited by 184 (16 self)
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In this paper, we address the problem of robust data association for simultaneous vehicle localization and map building. We show that the classical gated nearest neighbor approach, which considers each matching between sensor observations and features independently, ignores the fact that measurement prediction errors are correlated. This leads to easily accepting incorrect matchings when clutter or vehicle errors increase. We propose a new measurement of the joint compatibility of a set of pairings that successfully rejects spurious matchings. We show experimentally that this restrictive criterion can be used to efficiently search for the best solution to data association. Unlike the nearest neighbor, this method provides a robust solution in complex situations, such as cluttered environments or when revisiting previously mapped regions.
Ubiquitous networking robotics in urban settings
 In Proceedings of the IEEE/RSJ IROS Workshop on Network Robot Systems
"... Abstract In this paper we will present the objectives of a ..."
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Cited by 14 (7 self)
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Abstract In this paper we will present the objectives of a
CuikSlam: A kinematicsbased approach to SLAM
 in IEEE International Conference on Robotics and Automation, 2005
"... Abstract — In this paper, we depart from the fact that Simultaneous Localization and Mapping (SLAM) is a subcase of the general kinematic problem, and, thus, all techniques used in kinematics are potentially applicable to SLAM. We describe how to formalize a SLAM problem as a typical kinematic prob ..."
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Cited by 13 (7 self)
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Abstract — In this paper, we depart from the fact that Simultaneous Localization and Mapping (SLAM) is a subcase of the general kinematic problem, and, thus, all techniques used in kinematics are potentially applicable to SLAM. We describe how to formalize a SLAM problem as a typical kinematic problem and we propose a simple SLAM algorithm based on an intervalbased kinematic method called Cuik previously developed in our group. This new algorithm solves the SLAM problem taking advantage of the structure imposed in the SLAM problem by the motion and sensing capabilities of the autonomous robots. However, since we use a kinematic approach instead of a probabilistic one (the usual approach for SLAM) we can perfectly model the constraints between robot poses and between robot poses and landmarks, including the nonlinearities, and we can ensure those constraints to be fulfilled at any time during the map construction and refinement. The viability of the new algorithm is shown with a small test. Index Terms — SLAM, Kinematics, Intervalbased methods. I.
Map Management for Efficient Simultaneous Localization and Mapping (SLAM)
, 2002
"... The solution to the simultaneous localization and map building (SLAM) problem where an autonomous vehicle starts in an unknown location in an unknown environment and then incrementally build a map of landmarks present in this environment while simultaneously using this map to compute absolute vehicl ..."
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Cited by 5 (0 self)
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The solution to the simultaneous localization and map building (SLAM) problem where an autonomous vehicle starts in an unknown location in an unknown environment and then incrementally build a map of landmarks present in this environment while simultaneously using this map to compute absolute vehicle location is now well understood. Although a number of SLAM implementations have appeared in the recent literature, the need to maintain the knowledge of the relative relationships between all the landmark location estimates contained in the map makes SLAM computationally intractable in implementations containing more than a few tens of landmarks. This paper presents the theoretical basis and a practical implementation of a feature selection strategy that significantly reduces the computation requirements for SLAM. The paper shows that it is indeed possible to remove a large percentage of the landmarks from the map without making the map building process statistically inconsistent. Furthermore, it is shown that the computational cost of the SLAM algorithm can be reduced by judicious selection of landmarks to be preserved in the map.
Efficient Construction of Globally Consistent Ladar Maps Using Pose Network Topology and Nonlinear Programming
, 2003
"... Many instances of the mobile robot guidance mapping problem exhibit a topology that can be represented as a graph of nodes (observations) connected by edges (poses). We show that a cycle basis of this pose network can be used to generate the independent constraint equations in a natural constrained ..."
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Cited by 4 (0 self)
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Many instances of the mobile robot guidance mapping problem exhibit a topology that can be represented as a graph of nodes (observations) connected by edges (poses). We show that a cycle basis of this pose network can be used to generate the independent constraint equations in a natural constrained optimization formulation of the mapping problem. Explicit reasoning about the loop topology of the network can automatically generate such a cycle basis in linear time. Furthermore, in many practical cases, the pose network has sparse structure and the associated equations can then be solved time linear in the number of images. This approach can be used to construct globally consistent maps on very large scales in very limited computation. While the technique is applicable to mapbuilding in general, and even optimization in general, it is illustrated here for batch processing of 2D ladar scans into a mobile robot guidance map.
Dslam: Decoupled localization and mapping for autonomous robots
 Proceedings of the 12th International Symposium of Robotics Research
, 2005
"... Abstract. The main contribution of this paper is the reformulation of the simultaneous localization and mapping (SLAM) problem for mobile robots such that the mapping and localization can be treated as two concurrent yet separated processes: DSLAM (decoupled SLAM). It is shown that SLAM can be deco ..."
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Cited by 3 (0 self)
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Abstract. The main contribution of this paper is the reformulation of the simultaneous localization and mapping (SLAM) problem for mobile robots such that the mapping and localization can be treated as two concurrent yet separated processes: DSLAM (decoupled SLAM). It is shown that SLAM can be decoupled into solving a nonlinear static estimation problem for mapping and a lowdimensional dynamic estimation problem for localization. The mapping problem can be solved using an Extended Information Filter where the information matrix is shown to be exactly sparse. A significant saving in the computational effort can be achieved for large scale problems by exploiting the special properties of sparse matrices. An important feature of DSLAM is that the correlation among landmarks are still kept and it is demonstrated that the uncertainty of the map landmarks monotonically decrease. The algorithm is illustrated through computer simulations and experiments. 1
Efficient construction of optimal and consistent ladar maps using pose network topology and nonlinear programming
 in Proceedings of the 11th International Symposium of Robotics Research
, 2003
"... Abstract. Many forms of mobile robot guidance map can be visualized as a network of nodes (images) connected by edges (poses). We show how explicit reasoning about the loop topology of this pose network can generate a cycle basis for the network in linear time. This basis can then be used to form th ..."
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Cited by 3 (0 self)
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Abstract. Many forms of mobile robot guidance map can be visualized as a network of nodes (images) connected by edges (poses). We show how explicit reasoning about the loop topology of this pose network can generate a cycle basis for the network in linear time. This basis can then be used to form the independent constraint equations in a natural constrained optimization formulation of the mapping problem. In many practical cases, the pose network has sparse structure, and the associated equations can then be solved in linear time. This approach can be used to construct optimal, consistent maps on very large scales in very limited computation. While the technique is applicable to mapbuilding in general, and even optimization in general, it is illustrated here for batch processing of 2D ladar scans into a mobile robot guidance map.
Six DoF Decentralised SLAM
, 2003
"... This paper presents a decentralised multivehicle application of simultaneous localisation and mapping (SLAM) for unmanned aerial vehicles (UAVs). The UAVs are equipped with inertial measurement units (IMU), to determine the vehicles position, velocity and attitude, and vision cameras to detect ..."
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Cited by 2 (0 self)
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This paper presents a decentralised multivehicle application of simultaneous localisation and mapping (SLAM) for unmanned aerial vehicles (UAVs). The UAVs are equipped with inertial measurement units (IMU), to determine the vehicles position, velocity and attitude, and vision cameras to detect features in the environment.