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20
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.
iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree
"... We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique tree, a Bayes tree encodes a factored probabili ..."
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Cited by 71 (26 self)
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We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique tree, a Bayes tree encodes a factored probability density, but unlike the clique tree it is directed and maps more naturally to the square root information matrix of the simultaneous localization and mapping (SLAM) problem. In this paper, we highlight three insights provided by our new data structure. First, the Bayes tree provides a better understanding of the matrix factorization in terms of probability densities. Second, we show how the fairly abstract updates to a matrix factorization translate to a simple editing of the Bayes tree and its conditional densities. Third, we apply the Bayes tree to obtain a completely novel algorithm for sparse nonlinear incremental optimization, named iSAM2, which achieves improvements in efficiency through incremental variable reordering and fluid relinearization, eliminating the need for periodic batch steps. We analyze various properties of iSAM2 in detail, and show on a range of real and simulated datasets that our algorithm compares favorably with other recent mapping algorithms in both quality and efficiency.
Distributed metric calibration of ad hoc camera networks
 ACM Trans. Sen. Netw
, 2006
"... We discuss how to automatically obtain the metric calibration of an adhoc network of cameras with no centralized processor. We model the set of uncalibrated cameras as nodes in a communication network, and propose a distributed algorithm in which each camera performs a local, robust bundle adjustme ..."
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Cited by 39 (4 self)
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We discuss how to automatically obtain the metric calibration of an adhoc network of cameras with no centralized processor. We model the set of uncalibrated cameras as nodes in a communication network, and propose a distributed algorithm in which each camera performs a local, robust bundle adjustment over the camera parameters and scene points of its neighbors in an overlay “vision graph”. We analyze the performance of the algorithm on both simulated and real data, and show that the distributed algorithm results in a fairer allocation of messages per node while achieving comparable calibration accuracy to centralized bundle adjustment.
Closing a millionlandmarks loop
 In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing. submitted
, 2006
"... Abstract — We present an improved version of the treemap SLAM algorithm which uses Cholesky factors for representing Gaussians and a Hierarchical Tree Partitioning algorithm derived from the established KernighanLin heuristic for graph bisection. We demonstrate the algorithm’s efficiency by mapping ..."
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Cited by 35 (5 self)
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Abstract — We present an improved version of the treemap SLAM algorithm which uses Cholesky factors for representing Gaussians and a Hierarchical Tree Partitioning algorithm derived from the established KernighanLin heuristic for graph bisection. We demonstrate the algorithm’s efficiency by mapping a simulated building with 1032271 landmarks. In the end, we close a millionlandmarks loop in 21ms, providing an estimate for ≈10000 selected landmarks close to the robot, or in 442ms for computing a full estimate. I.
Adaptive teams of autonomous aerial and ground robots for situational awareness
 Journal of Robotic Systems
, 2007
"... In this paper, we present the component technologies and the integration of these technologies for the development of an adaptive system of heterogeneous robots for urban surveillance. In our integrated experiment and demonstration, aerial robots generate maps that are used to design navigation cont ..."
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Cited by 27 (3 self)
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In this paper, we present the component technologies and the integration of these technologies for the development of an adaptive system of heterogeneous robots for urban surveillance. In our integrated experiment and demonstration, aerial robots generate maps that are used to design navigation controllers and plan missions for the team. A team of ground robots constructs a radio signal strength map that is used as an aid for planning missions. Multiple robots are to establish a mobile, adhoc communication network that is aware of the radio signal strength between nodes and adapts to changing conditions to maintain connectivity. Finally, the team of aerial and ground robots is able to monitor a small village, and search for and localize human targets by the color of the uniform, while ensuring that the information from the team is availableto a remotely located human operator. The key component technologies and contributions include (a) mission specification and planning software; (b) decentralized control for navigation in an urban environment while maintaining communication; (c) programming abstractions and composition of controllers for multirobot deployment; (d) cooperative control strategies for search, identification, and localization of targets; and (e) threedimensional mapping in an urban setting. 1
Calibrating Distributed Camera Networks Using Belief Propagation
"... We discuss how to obtain the accurate and globally consistent selfcalibration of a distributed camera network, in which camera nodes with no centralized processor may be spread over a wide geographical area. We present a distributed calibration algorithm based on belief propagation, in which each c ..."
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Cited by 17 (2 self)
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We discuss how to obtain the accurate and globally consistent selfcalibration of a distributed camera network, in which camera nodes with no centralized processor may be spread over a wide geographical area. We present a distributed calibration algorithm based on belief propagation, in which each camera node communicates only with its neighbors that image a sufficient number of scene points. The natural geometry of the system and the formulation of the estimation problem give rise to statistical dependencies that can be efficiently leveraged in a probabilistic framework. The camera calibration problem poses several challenges to information fusion, including overdetermined parameterizations and nonaligned coordinate systems. We suggest practical approaches to overcome these difficulties, and demonstrate the accurate and consistent performance of the algorithm using a simulated 30node camera network with varying levels of noise in the correspondences used for calibration, as well as an experiment with 15 real images. I.
Efficient 6DOF SLAM with treemap as a generic backend
 in Proc. IEEE Int. Conf. Robot. and Automation
, 2007
"... AbstractTreemap is a generic SLAM algorithm that has been successfully used to estimate extremely large 2D maps closing a loop over a million landmarks in 442ms. We are currently working on an opensource implementation that can handle most variants of SLAM. In this paper we discuss the generic pa ..."
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Cited by 15 (2 self)
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AbstractTreemap is a generic SLAM algorithm that has been successfully used to estimate extremely large 2D maps closing a loop over a million landmarks in 442ms. We are currently working on an opensource implementation that can handle most variants of SLAM. In this paper we discuss the generic part of the algorithm constituting the treemap backend and the variant specific parts acting as a driver. We present their interplay from a softwareengineering point of view and show results for the case of 6DOF feature based SLAM, closing a simulated loop over 106657 3D features in 209ms.
Exploiting locality by nested dissection for square root smoothing and mapping
 In Robotics: Science and Systems (RSS
, 2006
"... The problem of creating a map given only the erroneous odometry and feature measurements and locating the own position in this environment is known in the literature as the Simultaneous Localization and Mapping (SLAM) problem. In this paper we investigate how a Nested Dissection Ordering scheme can ..."
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Cited by 14 (8 self)
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The problem of creating a map given only the erroneous odometry and feature measurements and locating the own position in this environment is known in the literature as the Simultaneous Localization and Mapping (SLAM) problem. In this paper we investigate how a Nested Dissection Ordering scheme can improve the the performance of a recently proposed Square Root Information Smoothing (SRIS) approach. As the SRIS does perform smoothing rather than filtering the SLAM problem becomes the Smoothing and Mapping problem (SAM). The computational complexity of the SRIS solution is dominated by the cost of transforming a matrix of all measurements into a square root form through factorization. The factorization of a fully dense measurement matrix has a cubic complexity in the worst case. We show that the computational complexity for the factorization of typical measurement matrices occurring in the SAM problem can be bound tighter under reasonable assumptions. Our work is motivated both from a numerical / linear algebra standpoint as well as by submaps used in EKF solutions to SLAM.
Calibrating Distributed Camera Networks
, 2008
"... Recent developments in wireless sensor networks have made feasible distributed camera networks, in which cameras and processing nodes may be spread over a wide geographical area, with no centralized processor and limited ability to communicate a large amount of information over long distances. This ..."
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Cited by 14 (1 self)
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Recent developments in wireless sensor networks have made feasible distributed camera networks, in which cameras and processing nodes may be spread over a wide geographical area, with no centralized processor and limited ability to communicate a large amount of information over long distances. This paper overviews distributed algorithms for the calibration of such camera networks that is, the automatic estimation of each camera’s position, orientation, and focal length. In particular, we discuss a decentralized method for obtaining the vision graph for a distributed camera network, in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. We next describe a distributed algorithm in which each camera performs a local, robust nonlinear optimization over the camera parameters and scene points of its vision graph neighbors to obtain an initial calibration estimate. We then show how a distributed inference algorithm based on belief propagation can refine the initial estimate to be both accurate and globally consistent.
Concurrent filtering and smoothing
 in Intl. Conf. on Information Fusion (FUSION
, 2012
"... Abstract—This paper presents a novel algorithm for integrating realtime filtering of navigation data with full map/trajectory smoothing. Unlike conventional mapping strategies, the result of loop closures within the smoother serve to correct the realtime navigation solution in addition to the map. ..."
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Cited by 6 (5 self)
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Abstract—This paper presents a novel algorithm for integrating realtime filtering of navigation data with full map/trajectory smoothing. Unlike conventional mapping strategies, the result of loop closures within the smoother serve to correct the realtime navigation solution in addition to the map. This solution views filtering and smoothing as different operations applied within a single graphical model known as a Bayes tree. By maintaining all information within a single graph, the optimal linear estimate is guaranteed, while still allowing the filter and smoother to operate asynchronously. This approach has been applied to simulated aerial vehicle sensors consisting of a highspeed IMU and stereo camera. Loop closures are extracted from the vision system in an external process and incorporated into the smoother when discovered. The performance of the proposed method is shown to approach that of full batch optimization while maintaining realtime operation. Index Terms—Navigation, smoothing, filtering, loop closing, Bayes tree, factor graph Figure 1. Smoother and filter combined in a single optimization problem and represented as a Bayes tree. A separator is selected so as to enable parallel computations. I.