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446
Markov Chain Monte Carlo Data Association for MultiTarget Tracking Univ
, 2008
"... data association (MCMCDA) for solving data association problems arising in multitarget tracking in a cluttered environment. When the number of targets is fixed, the singlescan version of MCMCDA approximates joint probabilistic data association (JPDA). Although the exact computation of association ..."
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Cited by 129 (22 self)
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data association (MCMCDA) for solving data association problems arising in multitarget tracking in a cluttered environment. When the number of targets is fixed, the singlescan version of MCMCDA approximates joint probabilistic data association (JPDA). Although the exact computation of association probabilities in JPDA is NPhard, we prove that the singlescan MCMCDA algorithm provides a fully polynomial randomized approximation scheme for JPDA. For general multitarget tracking problems, in which unknown numbers of targets appear and disappear at random times, we present a multiscan MCMCDA algorithm that approximates the optimal Bayesian filter. We also present extensive simulation studies supporting theoretical results in this paper. Our simulation results also show that MCMCDA outperforms multiple hypothesis tracking (MHT) by a significant margin in terms of accuracy and efficiency under extreme conditions, such as a large number of targets in a dense environment, low detection probabilities, and high false alarm rates. Index Terms—Joint probabilistic data association (JPDA), Markov chain Monte Carlo data association (MCMCDA), multiple hypothesis tracking (MHT). I.
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 121 (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
Monte Carlo Filtering for MultiTarget Tracking and Data Association
 IEEE Transactions on Aerospace and Electronic Systems
, 2004
"... In this paper we present Monte Carlo methods for multitarget tracking and data association. The methods are applicable to general nonlinear and nonGaussian models for the target dynamics and measurement likelihood. We provide efficient solutions to two very pertinent problems: the data associat ..."
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Cited by 73 (4 self)
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In this paper we present Monte Carlo methods for multitarget tracking and data association. The methods are applicable to general nonlinear and nonGaussian models for the target dynamics and measurement likelihood. We provide efficient solutions to two very pertinent problems: the data association problem that arises due to unlabelled measurements in the presence of clutter, and the curse of dimensionality that arises due to the increased size of the statespace associated with multiple targets. We develop a number of algorithms to achieve this. The first, which we will refer to as the Monte Carlo Joint Probabilistic Data Association Filter (MCJPDAF), is a generalisation of the strategy proposed in [1], [2]. As is the case for the JPDAF, the distributions of interest are the marginal filtering distributions for each of the targets, but these are approximated with particles rather than Gaussians. We also develop two extensions to the standard particle filtering methodology for tracking multiple targets. The first, which we will refer to as the Sequential Sampling Particle Filter (SSPF), samples the individual targets sequentially by utilising a factorisation of the importance weights. The second, which we will refer to as the Independent Partition Particle Filter (IPPF), assumes the associations to be independent over the individual targets, leading to an efficient componentwise sampling strategy to construct new particles. We evaluate and compare the proposed methods on a challenging synthetic tracking problem.
Visualinertial sensor fusion: Localization, mapping and sensortosensor selfcalibration
 International Journal of Robotics Research
, 2011
"... Visual and inertial sensors, in combination, are able to provide accurate motion estimates and are wellsuited for use in many robot navigation tasks. However, correct data fusion, and hence overall performance, depends on careful calibration of the rigid body transform between the sensors. Obtainin ..."
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Cited by 51 (3 self)
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Visual and inertial sensors, in combination, are able to provide accurate motion estimates and are wellsuited for use in many robot navigation tasks. However, correct data fusion, and hence overall performance, depends on careful calibration of the rigid body transform between the sensors. Obtaining this calibration information is typically difficult and timeconsuming, and normally requires additional equipment. In this paper we describe an algorithm, based on the unscented Kalman filter, for selfcalibration of the transform between a camera and an inertial measurement unit (IMU). Our formulation rests on a differential geometric analysis of the observability of the cameraIMU system; this analysis shows that the sensortosensor transform, the IMU gyroscope and accelerometer biases, the local gravity vector, and the metric scene structure can be recovered from camera and IMU measurements alone. While calibrating the transform we simultaneously localize the IMU and build a map of the surroundings – all without additional hardware or prior knowledge about the environment in which a robot is To Appear in International Journal of Robotics Research operating. We present results from simulation studies and from experiments with a monocular camera and a lowcost IMU, which demonstrate accurate estimation of both the calibration parameters and the local scene structure. 1 1
SOIKF: Distributed Kalman Filtering With LowCost Communications Using the Sign of Innovations
 IEEE TRANSACTIONS ON SIGNAL PROCESSING
, 2006
"... When dealing with decentralized estimation, it is important to reduce the cost of communicating the distributed observations—a problem receiving revived interest in the context of wireless sensor networks. In this paper, we derive and analyze distributed state estimators of dynamical stochastic proc ..."
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Cited by 50 (12 self)
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When dealing with decentralized estimation, it is important to reduce the cost of communicating the distributed observations—a problem receiving revived interest in the context of wireless sensor networks. In this paper, we derive and analyze distributed state estimators of dynamical stochastic processes, whereby the low communication cost is effected by requiring the transmission of a single bit per observation. Following a Kalman filtering (KF) approach, we develop recursive algorithms for distributed state estimation based on the sign of innovations (SOI). Even though SOIKF can afford minimal communication overhead, we prove that in terms of performance and complexity it comes very close to the clairvoyant KF which is based on the analogamplitude observations. Reinforcing our conclusions, we show that the SOIKF applied to distributed target tracking based on distanceonly observations yields accurate estimates at low communication cost.
Smoothing algorithms for statespace models
 in Submission IEEE Transactions on Signal Processing
, 2004
"... A prevalent problem in statistical signal processing, applied statistics, and time series analysis is the calculation of the smoothed posterior distribution, which describes the uncertainty associated with a state, or a sequence of states, conditional on data from the past, the present, and the futu ..."
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Cited by 47 (8 self)
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A prevalent problem in statistical signal processing, applied statistics, and time series analysis is the calculation of the smoothed posterior distribution, which describes the uncertainty associated with a state, or a sequence of states, conditional on data from the past, the present, and the future. The aim of this paper is to provide a rigorous foundation for the calculation, or approximation, of such smoothed distributions, to facilitate a robust and efficient implementation. Through a cohesive and generic exposition of the scientific literature we offer several novel extensions such that one can perform smoothing in the most general case. Experimental results for: a Jump Markov Linear System; a comparison of particle smoothing methods; and parameter estimation using a particle implementation of the EM algorithm, are provided.
RaoBlackwellized Particle Filter for Multiple Target Tracking
 Information Fusion Journal
, 2005
"... In this article we propose a new RaoBlackwellized particle filtering based algorithm for tracking an unknown number of targets. The algorithm is based on formulating probabilistic stochastic process models for target states, data associations, and birth and death processes. The tracking of these st ..."
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Cited by 45 (4 self)
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In this article we propose a new RaoBlackwellized particle filtering based algorithm for tracking an unknown number of targets. The algorithm is based on formulating probabilistic stochastic process models for target states, data associations, and birth and death processes. The tracking of these stochastic processes is implemented using sequential Monte Carlo sampling or particle filtering, and the e#ciency of the Monte Carlo sampling is improved by using RaoBlackwellization.
Particle filter theory and practice with positioning applications
 IEEE Aerospace and Electronic Systems Magazine
, 2010
"... N.B.: When citing this work, cite the original article. ©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to ..."
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Cited by 43 (11 self)
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N.B.: When citing this work, cite the original article. ©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted
Robocentric map joining: Improving the consistency of EKFSLAM
, 2007
"... In this paper we study the Extended Kalman Filter approach to simultaneous localization and mapping (EKFSLAM), describing its known properties and limitations, and concentrate on the filter consistency issue. We show that linearization of the inherent nonlinearities of both the vehicle motion and t ..."
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Cited by 39 (3 self)
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In this paper we study the Extended Kalman Filter approach to simultaneous localization and mapping (EKFSLAM), describing its known properties and limitations, and concentrate on the filter consistency issue. We show that linearization of the inherent nonlinearities of both the vehicle motion and the sensor models frequently drives the solution of the EKFSLAM out of consistency, specially in those situations where uncertainty surpasses a certain threshold. We propose a mapping algorithm, Robocentric Map Joining, which improves consistency of the EKFSLAM algorithm by limiting the level of uncertainty in the continuous evolution of the stochastic map: (1) by building a sequence of independent local maps, and (2) by using a robot centered representation of each local map. Simulations and a largescale indoor/outdoor experiment validate the proposed approach.
Analytic implementations of the cardinalized probability hypothesis density filter
 IEEE Transactions on Signal Processing
, 2007
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