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115
The Unscented Kalman Filter for nonlinear estimation
, 2000
"... The Extended Kalman Filter (EKF) has become a standard technique used in a number of nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system, estimating parameters for nonlinear system identification (e.g., learning the weights of a ne ..."
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Cited by 72 (4 self)
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The Extended Kalman Filter (EKF) has become a standard technique used in a number of nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system, estimating parameters for nonlinear system identification (e.g., learning the weights of a neural network), and dual estimation (e.g., the Expectation Maximization (EM) algorithm) where both states and parameters are estimated simultaneously. This paper points out the flaws in using the EKF, and introduces an improvement, the Unscented Kalman Filter (UKF), proposed by Julier and Uhlman [5]. A central and vital operation performed in the Kalman Filter is the propagation of a Gaussian random variable (GRV) through the system dynamics. In the EKF, the state distribution is approximated
A tutorial on particle filtering and smoothing: fifteen years later
 OXFORD HANDBOOK OF NONLINEAR FILTERING
, 2011
"... Optimal estimation problems for nonlinear nonGaussian statespace models do not typically admit analytic solutions. Since their introduction in 1993, particle filtering methods have become a very popular class of algorithms to solve these estimation problems numerically in an online manner, i.e. r ..."
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Cited by 70 (9 self)
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Optimal estimation problems for nonlinear nonGaussian statespace models do not typically admit analytic solutions. Since their introduction in 1993, particle filtering methods have become a very popular class of algorithms to solve these estimation problems numerically in an online manner, i.e. recursively as observations become available, and are now routinely used in fields as diverse as computer vision, econometrics, robotics and navigation. The objective of this tutorial is to provide a complete, uptodate survey of this field as of 2008. Basic and advanced particle methods for filtering as well as smoothing are presented.
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 66 (2 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
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 47 (3 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.
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 47 (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.
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 45 (5 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 43 (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.
Using the topological skeleton for scalable global metrical mapbuilding
 In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems
, 2004
"... Abstract — Most simultaneous localization and mapping (SLAM) approaches focus on purely metrical approaches to mapbuilding. We present a method for computing the global metrical map that builds on the structure provided by a topological map. This allows us to factor the uncertainty in the map into ..."
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Cited by 31 (11 self)
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Abstract — Most simultaneous localization and mapping (SLAM) approaches focus on purely metrical approaches to mapbuilding. We present a method for computing the global metrical map that builds on the structure provided by a topological map. This allows us to factor the uncertainty in the map into local metrical uncertainty (which is handled well by existing SLAM methods), global topological uncertainty (which is handled well by recently developed topological maplearning methods), and global metrical uncertainty (which can be handled effectively once the other types of uncertainty are factored out). We believe that this method for building the global metrical map will be scalable to very large environments. I.
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 31 (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.