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214
An Efficient FastSLAM Algorithm for Generating Maps of LargeScale Cyclic . . .
 IN PROC. OF THE IEEE/RSJ INT. CONF. ON INTELLIGENT ROBOTS AND SYSTEMS (IROS
, 2003
"... The ability to learn a consistent model of its environment is a prerequisite for autonomous mobile robots. A particularly challenging problem in acquiring environment maps is that of closing loops; loops in the environment create challenging data association problems [9]. This paper presents a novel ..."
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Cited by 123 (23 self)
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The ability to learn a consistent model of its environment is a prerequisite for autonomous mobile robots. A particularly challenging problem in acquiring environment maps is that of closing loops; loops in the environment create challenging data association problems [9]. This paper presents a novel algorithm that combines RaoBlackwellized particle filtering and scan matching. In our approach scan matching is used for minimizing odometric errors during mapping. A probabilistic model of the residual errors of scan matching process is then used for the resampling steps. This way the number of samples required is seriously reduced. Simultaneously we reduce the particle depletion problem that typically prevents the robot from closing large loops. We present extensive experiments that illustrate the superior performance of our approach compared to previous approaches.
Scalable monocular SLAM
 in IEEE Computer Society Conference on Computer Vision and Pattern Recognition
, 2006
"... Localization and mapping in unknown environments becomes more difficult as the complexity of the environment increases. With conventional techniques, the cost of maintaining estimates rises rapidly with the number of landmarks mapped. We present a monocular SLAM system that employs a particle filter ..."
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Cited by 116 (3 self)
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Localization and mapping in unknown environments becomes more difficult as the complexity of the environment increases. With conventional techniques, the cost of maintaining estimates rises rapidly with the number of landmarks mapped. We present a monocular SLAM system that employs a particle filter and topdown search to allow realtime performance while mapping large numbers of landmarks. To our knowledge, we are the first to apply this FastSLAMtype particle filter to singlecamera SLAM. We also introduce a novel partial initialization procedure that efficiently determines the depth of new landmarks. Moreover, we use information available in observations of new landmarks to improve camera pose estimates. Results show the system operating in realtime on a standard workstation while mapping hundreds of landmarks. 1.
ModelBased Hand Tracking Using A Hierarchical Bayesian Filter
, 2004
"... This thesis focuses on the automatic recovery of threedimensional hand motion from one or more views. A 3D geometric hand model is constructed from truncated cones, cylinders and ellipsoids and is used to generate contours, which can be compared with edge contours and skin colour in images. The han ..."
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Cited by 103 (3 self)
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This thesis focuses on the automatic recovery of threedimensional hand motion from one or more views. A 3D geometric hand model is constructed from truncated cones, cylinders and ellipsoids and is used to generate contours, which can be compared with edge contours and skin colour in images. The hand tracking problem is formulated as state estimation, where the model parameters define the internal state, which is to be estimated from image observations. In thew first
SigmaPoint Kalman Filters for Probabilistic Inference in Dynamic StateSpace Models
 In Proceedings of the Workshop on Advances in Machine Learning
, 2003
"... Probabilistic inference is the problem of estimating the hidden states of a system in an optimal and consistent fashion given a set of noisy or incomplete observations. The optimal solution to this problem is given by the recursive Bayesian estimation algorithm which recursively updates the post ..."
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Cited by 78 (5 self)
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Probabilistic inference is the problem of estimating the hidden states of a system in an optimal and consistent fashion given a set of noisy or incomplete observations. The optimal solution to this problem is given by the recursive Bayesian estimation algorithm which recursively updates the posterior density of the system state as new observations arrive online.
Mobile Robot Localisation and Mapping in Extensive Outdoor Environments
, 2002
"... This thesis addresses the issues of scale for practical implementations of simultaneous localisation and mapping (SLAM) in extensive outdoor environments. Building an incremental map while also using it for localisation is of prime importance for mobile robot navigation but, until recently, has bee ..."
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Cited by 67 (8 self)
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This thesis addresses the issues of scale for practical implementations of simultaneous localisation and mapping (SLAM) in extensive outdoor environments. Building an incremental map while also using it for localisation is of prime importance for mobile robot navigation but, until recently, has been confined to smallscale, mostly indoor, environments. The critical problems for largescale implementations are as follows. First, data association finding correspondences between map landmarks and robot sensor measurementsbecomes difficult in complex, cluttered environments, especially if the robot location is uncertain. Second, the information required to maintain a consistent map using traditional methods imposes a prohibitive computational burden as the map increases in size. And third, the mathematics for SLAM relies on assumptions of small errors and nearlinearity, and these become invalid for larger maps.
Exactly sparse extended information filters for featurebased SLAM
 Proceedings of the IJCAI Workshop on Reasoning with Uncertainty in Robotics
, 2001
"... Recent research concerning the Gaussian canonical form for Simultaneous Localization and Mapping (SLAM) has given rise to a handful of algorithms that attempt to solve the SLAM scalability problem for arbitrarily large environments. One such estimator that has received due attention is the Sparse Ex ..."
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Cited by 66 (12 self)
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Recent research concerning the Gaussian canonical form for Simultaneous Localization and Mapping (SLAM) has given rise to a handful of algorithms that attempt to solve the SLAM scalability problem for arbitrarily large environments. One such estimator that has received due attention is the Sparse Extended Information Filter (SEIF) by Thrun et al., which is reported to be nearly constant time, irrespective of the size of the map. The key to the SEIF’s scalability is to prune weak links in what is a dense information (inverse covariance) matrix to achieve a sparse approximation that allows for efficient, scalable SLAM. We demonstrate that the SEIF sparsification strategy yields error estimates that are overconfident when expressed in the global reference frame, while empirical results show that relative map consistency is maintained. In this paper, we propose an alternative scalable estimator based in the information form that maintains sparsity while preserving consistency. The paper describes a method for controlling the population of the information matrix, whereby we track a modified version of the SLAM posterior, essentially by ignoring a small fraction of temporal measurements. In this manner, the Exactly Sparse Extended Information Filter (ESEIF) performs inference over a model that is conservative relative to the standard Gaussian distribution. We compare our algorithm to the SEIF and standard EKF both in simulation as well as on two nonlinear datasets. The results convincingly show that our method yields conservative estimates for the robot pose and map that are nearly identical to those of the EKF.
RaoBlackwellised Particle Filtering for Fault Diagnosis
 IEEE Aerospace
, 2001
"... We tackle the fault diagnosis problem using conditionally Gaussian state space models and an efficient Monte Carlo method known as RaoBlackwellised particle filtering. In this setting, there is one different linearGaussian state space model for each possible discrete state of operation. The task of ..."
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Cited by 60 (1 self)
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We tackle the fault diagnosis problem using conditionally Gaussian state space models and an efficient Monte Carlo method known as RaoBlackwellised particle filtering. In this setting, there is one different linearGaussian state space model for each possible discrete state of operation. The task of diagnosis is to identify the discrete state of operation using the continuous measurements corrupted by Gaussian noise.
Robust visual tracking for multiple targets
 IN ECCV
, 2006
"... We address the problem of robust multitarget tracking within the application of hockey player tracking. Although there has been extensive work in multitarget tracking, there is no existing visual tracking system that can automatically and robustly track a variable number of targets and correctly m ..."
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Cited by 48 (5 self)
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We address the problem of robust multitarget tracking within the application of hockey player tracking. Although there has been extensive work in multitarget tracking, there is no existing visual tracking system that can automatically and robustly track a variable number of targets and correctly maintain their identities with a monocular camera regardless of background clutter, camera motion and frequent mutual occlusion between targets. We build our system on the basis of the previous work by Okuma et al. [OTdF + 04]. The particle filter technique is adopted and modified to fit into the multitarget tracking framework. A rectification technique is employed to map the locations of players from the video frame coordinate system to the standard hockey rink coordinates so that the system can compensate for camera motion and the dynamics of players on the rink can be improved by a second order autoregression model. A global nearest neighbor data association algorithm is introduced to assign boosting detections to the existing tracks for the proposal distribution in particle filters. The meanshift algorithm is embedded into the particle filter framework to stabilize the trajectories of the targets for robust tracking during mutual occlusion. The color model of the targets is also improved by the kernel introduced by meanshift. Experimental results show that our system is able to correctly track all the targets in the scene even if they are partially or completely occluded for a period of time.
FastSLAM: An efficient solution to the simultaneous localization and mapping problem with unknown data association
 Journal of Machine Learning Research
"... This article provides a comprehensive description of FastSLAM, a new family of algorithms for the simultaneous localization and mapping problem, which specifically address hard data association problems. The algorithm uses a particle filter for sampling robot paths, and extended Kalman filters for r ..."
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Cited by 41 (0 self)
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This article provides a comprehensive description of FastSLAM, a new family of algorithms for the simultaneous localization and mapping problem, which specifically address hard data association problems. The algorithm uses a particle filter for sampling robot paths, and extended Kalman filters for representing maps acquired by the vehicle. This article presents two variants of this algorithm, the original algorithm along with a more recent variant that provides improved performance in certain operating regimes. In addition to a mathematical derivation of the new algorithm, we present a proof of convergence and experimental results on its performance on realworld data. 1