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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.
Simultaneous Mapping and Localization With Sparse Extended Information Filters: Theory and Initial Results
, 2002
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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.
SLAM updates require constant time
 In Workshop on the Algorithmic Foundations of Robotics
, 2002
"... Abstract. This paper describes a scalable algorithm for the simultaneous mapping and localization (SLAM) problem. SLAM is the problem of determining the location of environmental features with a roving robot. Many of today’s popular techniques are based on extended Kalman filters (EKFs), which requi ..."
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Cited by 9 (1 self)
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Abstract. This paper describes a scalable algorithm for the simultaneous mapping and localization (SLAM) problem. SLAM is the problem of determining the location of environmental features with a roving robot. Many of today’s popular techniques are based on extended Kalman filters (EKFs), which require update time quadratic in the number of features in the map. This paper develops the notion of sparse extended information filters (SEIFs), as a new method for solving the SLAM problem. SEIFs exploit structure inherent in the SLAM problem, representing maps through local, Weblike networks of features. By doing so, updates can be performed in constant time, irrespective of the number of features in the map. This paper presents several original constanttime results of SEIFs, and provides simulation results that show the high accuracy of the resulting maps in comparison to the computationally more cumbersome EKF solution. 1
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.
Analysis of positioning uncertainty in simultaneous localization and mapping (SLAM
 in Proc. of the IEEE/RSJ International Conference on Robotics and Intelligent Systems (IROS
, 2004
"... Abstract — This paper studies the time evolution of the covariance of the position estimates in singlerobot Simultaneous Localization And Mapping (SLAM). A closedform expression is derived, that establishes a functional relation between the noise parameters of the robot’s proprioceptive and extero ..."
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Cited by 4 (2 self)
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Abstract — This paper studies the time evolution of the covariance of the position estimates in singlerobot Simultaneous Localization And Mapping (SLAM). A closedform expression is derived, that establishes a functional relation between the noise parameters of the robot’s proprioceptive and exteroceptive sensors, the number of features being mapped, and the attainable accuracy of SLAM. Furthermore, it is demonstrated how prior information about the spatial density of landmarks can be utilized in order to compute a tight upper bound on the expected covariance of the positioning errors. The derived closedform expressions enable the prediction of SLAM positioning performance, without resorting to extensive simulations, and thus offer an analytical tool for determining the sensor characteristics required to achieve a desired degree of accuracy. Simulation experiments are conducted, that corroborate the presented theoretical analysis. I.
Analytical characterization of the accuracy of slam without absolute orientation measurements
 In Proc. Robotics: Science and Systems Conf
, 2006
"... In this report we derive analytical upper bounds on the covariance of the state estimates in SLAM. The analysis is based on a novel formulation of the SLAM problem, that enables the simultaneous estimation of the landmark coordinates with respect to the a robotcentered frame (relative map), as well ..."
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Cited by 2 (2 self)
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In this report we derive analytical upper bounds on the covariance of the state estimates in SLAM. The analysis is based on a novel formulation of the SLAM problem, that enables the simultaneous estimation of the landmark coordinates with respect to the a robotcentered frame (relative map), as well as with respect to a fixed global frame (absolute map). A study of the properties of the covariance matrix in this formulation yields analytical upper bounds for the uncertainty of both map representations. Moreover, by employing results from Least Squares estimation theory, the guaranteed accuracy of the robot pose estimates is derived as a function of the accuracy of the robot’s sensors, and of the properties of the map. Contrary to previous approaches, the method presented here makes no assumptions about the availability of a sensor measuring the absolute orientation of the robot. The theoretical analysis is validated by simulation results and realworld experiments. 1
Visual Sliding Window SLAM with Application to Planetary Landers
"... This paper describes a Sliding Window Filter (SWF) that is a constanttime approximation to the featurebased batch nonlinear least squares Simultaneous Localization and Mapping (SLAM) problem. In particular, we are interested in improving the range resolution of stereo vision for Entry, Descent ..."
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This paper describes a Sliding Window Filter (SWF) that is a constanttime approximation to the featurebased batch nonlinear least squares Simultaneous Localization and Mapping (SLAM) problem. In particular, we are interested in improving the range resolution of stereo vision for Entry, Descent and Landing (EDL) missions to Mars and other planetary bodies. The goal is to create accurate and precise 3D planetary surface structure estimates by filtering sequences of stereo images taken from an autonomous landing vehicle. More generally we are interested in fast, optimal, relative spatial estimation for mobile robots. The SWF is useful in this context because it can scale from the offline, optimal batch least squares solution to fast online incremental solutions. For instance, if the window encompasses all time, the solution is algebraically equivalent to full SLAM; if only one time step is maintained, the solution is algebraically equivalent to the Extended Kalman Filter SLAM solution; if robot poses and environment landmarks are slowly marginalized out over time such that the state vector ceases to grow, then the filter becomes constant time, like Visual Odometry. Further, the sliding window method exhibits other interesting properties, like reversible data association, outof sequence measurement updates, and robust estimation across multiple timesteps. We test the SWF with image data captured to emulate EDL conditions for a Mars lander. Experiments show that structure estimates derived from the SWF converge to the optimal result predicted by theory. To the best of our knowledge, this is the first work to show optimal