Results 1  10
of
150
A computationally efficient method for largescale concurrent mapping and localization
, 2000
"... Decoupled stochastic mapping (DSM) is a computationally efficient approach to largescale concurrent mapping and localization. DSM reduces the computational burden of conventional stochastic mapping by dividing the environment into multiple overlapping submap regions, each with its own stochastic ma ..."
Abstract

Cited by 144 (6 self)
 Add to MetaCart
Decoupled stochastic mapping (DSM) is a computationally efficient approach to largescale concurrent mapping and localization. DSM reduces the computational burden of conventional stochastic mapping by dividing the environment into multiple overlapping submap regions, each with its own stochastic map. Two new approximation techniques are utilized for transferring vehicle state information from one submap to another, yielding a constanttime algorithm whose memory requirements scale linearly with the size of the operating area. The performance of two different variations of the algorithm is demonstrated through simulations of environments with 110 and 1200 features. Experimental results are presented for an environment with 93 features using sonar data obtained in a 3 by 9 by 1 meter testing tank.
Variational learning for switching statespace models
 Neural Computation
, 1998
"... We introduce a new statistical model for time series which iteratively segments data into regimes with approximately linear dynamics and learns the parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time series models  hidden Ma ..."
Abstract

Cited by 143 (6 self)
 Add to MetaCart
We introduce a new statistical model for time series which iteratively segments data into regimes with approximately linear dynamics and learns the parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time series models  hidden Markov models and linear dynamical systems  and is closely related to models that are widely used in the control and econometrics literatures. It can also be derived by extending the mixture of experts neural network (Jacobs et al., 1991) to its fully dynamical version, in which both expert and gating networks are recurrent. Inferring the posterior probabilities of the hidden states of this model is computationally intractable, and therefore the exact Expectation Maximization (EM) algorithm cannot be applied. However, we present a variational approximation that maximizes a lower bound on the log likelihood and makes use of both the forwardbackward recursions for hidden Markov models and the Kalman lter recursions for linear dynamical systems. We tested the algorithm both on artificial data sets and on a natural data set of respiration force from a patient with sleep apnea. The results suggest that variational approximations are a viable method for inference and learning in switching statespace models.
Markovian Models for Sequential Data
, 1996
"... Hidden Markov Models (HMMs) are statistical models of sequential data that have been used successfully in many machine learning applications, especially for speech recognition. Furthermore, in the last few years, many new and promising probabilistic models related to HMMs have been proposed. We firs ..."
Abstract

Cited by 84 (2 self)
 Add to MetaCart
Hidden Markov Models (HMMs) are statistical models of sequential data that have been used successfully in many machine learning applications, especially for speech recognition. Furthermore, in the last few years, many new and promising probabilistic models related to HMMs have been proposed. We first summarize the basics of HMMs, and then review several recent related learning algorithms and extensions of HMMs, including in particular hybrids of HMMs with artificial neural networks, InputOutput HMMs (which are conditional HMMs using neural networks to compute probabilities), weighted transducers, variablelength Markov models and Markov switching statespace models. Finally, we discuss some of the challenges of future research in this very active area. 1 Introduction Hidden Markov Models (HMMs) are statistical models of sequential data that have been used successfully in many applications in artificial intelligence, pattern recognition, speech recognition, and modeling of biological ...
A Framework for Speech Source Localization Using Sensor Arrays
, 1995
"... Electronically steerable arrays of microphones have avariety of uses in speech data acquisition systems. Applications include teleconferencing, speech recognition and speaker identification, sound capture in adverse environments, and biomedical devices for the hearing impaired. An array of microph ..."
Abstract

Cited by 52 (5 self)
 Add to MetaCart
Electronically steerable arrays of microphones have avariety of uses in speech data acquisition systems. Applications include teleconferencing, speech recognition and speaker identification, sound capture in adverse environments, and biomedical devices for the hearing impaired. An array of microphones has a number of advantages over a singlemicrophone system. It may be electronically aimed to provide a highquality signal from a desired source location while simultaneously attenuating interfering talkers and ambient noise, does not necessitate local placement of transducers or encumber the talker with a handheld or headmounted microphone, and does not require physical movement to alter its direction of reception. Additionally, it has capabilities that a single microphone does not; namely automatic detection, localization, and tracking of active talkers in its receptive area. A fundamental requirement of sensor array systems is the ability to locate and track a speech source. An accurate fix on the primary talker, as well as knowledge of any interfering talkers or coherent noise sources, is necessary to effectively steer the array. Source location data may also be used for purposes other than beamforming; e.g. aiming a camera in a videoconferencing system. In addition to high accuracy, the location estimator must be
Decoupled Stochastic Mapping
, 2001
"... This paper describes decoupled stochastic mapping (DSM), a new computationally efficient approach to largescale concurrent mapping and localization (CML). DSM reduces the computational burden of conventional stochastic mapping by dividing the environment into multiple overlapping submap regions, ea ..."
Abstract

Cited by 51 (9 self)
 Add to MetaCart
This paper describes decoupled stochastic mapping (DSM), a new computationally efficient approach to largescale concurrent mapping and localization (CML). DSM reduces the computational burden of conventional stochastic mapping by dividing the environment into multiple overlapping submap regions, each with its own stochastic map. Two new approximation techniques are utilized for transferring vehicle state information from one submap to another, yielding a constanttime algorithm whose memory requirements scale linearly with the number of submaps. The approach is demonstrated via simulations and experiments. Simulation results are presented for the case of an autonomous underwater vehicle (AUV) navigating in an unknown environments with 110 and 1200 features using simulated observations of point features by a forward look sonar. Empirical tests are used to examine the consistency of the error bounds calculated by the different methods. Experimental results are also presented for an environment with 93 features using sonar data obtained in a 3 by 9 by 1 m testing tank.
Realtime VisionBased Camera Tracking for Augmented Reality Applications
, 1997
"... Augmented reality deals with the problem of dynamically augmenting or enhancing (images or live video of) the real world with computer generated data (e.g., graphics of virtual objects). This poses two major problems: (a) determining the precise alignment of real and virtual coordinate frames for ov ..."
Abstract

Cited by 46 (4 self)
 Add to MetaCart
Augmented reality deals with the problem of dynamically augmenting or enhancing (images or live video of) the real world with computer generated data (e.g., graphics of virtual objects). This poses two major problems: (a) determining the precise alignment of real and virtual coordinate frames for overlay, and (b) capturing the 3D environment including camera and object motions. The latter is important for interactive augmented reality applications where users can interact with both real and virtual objects. Here we address the problem of accurately tracking the 3D motion of a monocular camera in a known 3D environment and dynamically estimating the 3D camera location. We utilize fully automated landmarkbased camera calibration to initialize the motion estimation and employ extended Kalman filter techniques to track landmarks and to estimate the camera location. The implementation of our approach has been proven to be efficient and robust and our system successfully tracks in realtim...
Switching StateSpace Models
 King’s College Road, Toronto M5S 3H5
, 1996
"... We introduce a statistical model for times series data with nonlinear dynamics which iteratively segments the data into regimes with approximately linear dynamics and learns the parameters of each of those regimes. This model combines and generalizes two of the most widely used stochastic time se ..."
Abstract

Cited by 41 (2 self)
 Add to MetaCart
We introduce a statistical model for times series data with nonlinear dynamics which iteratively segments the data into regimes with approximately linear dynamics and learns the parameters of each of those regimes. This model combines and generalizes two of the most widely used stochastic time series modelsthe hidden Markov model and the linear dynamical systemand is related to models that are widely used in the control and econometrics literatures. It can also be derived by extending the mixture of experts neural network model (Jacobs et al., 1991) to its fully dynamical version, in which both expert and gating networks are recurrent. Inferring the posterior probabilities of the hidden states of this model is computationally intractable, and therefore the exact Expectation Maximization (EM) alogithm cannot be applied. However, we present a variational approximation which maximizes a lower bound on the log likelihood and makes use of both the forwardbackward recursio...
Sensor fusion for mobile robot navigation
 Proceedings of the IEEE
, 1997
"... We review techniques for sensor fusion in robot navigation, emphasizing algorithms for selflocation. These find use when the sensor suite of a mobile robot comprises several different sensors, some complementary and some redundant. Integrating the sensor readings, the robot seeks to accomplish task ..."
Abstract

Cited by 37 (0 self)
 Add to MetaCart
We review techniques for sensor fusion in robot navigation, emphasizing algorithms for selflocation. These find use when the sensor suite of a mobile robot comprises several different sensors, some complementary and some redundant. Integrating the sensor readings, the robot seeks to accomplish tasks such as constructing a map of its environment, locating itself in that map, and recognizing objects that should be avoided or sought. Our review describes integration techniques in two categories: lowlevel fusion is used for direct integration of sensory data, resulting in parameter and state estimates; highlevel fusion is used for indirect integration of sensory data in hierarchical architectures, through command arbitration and integration of control signals suggested by different modules. The review provides an arsenal of tools for addressing this (rather illposed) problem in machine intelligence, including Kalman filtering, rulebased techniques, behavior based algorithms, and approaches that borrow from information theory, Dempster–Shafer reasoning, fuzzy logic and neural networks. It points to several furtherresearch needs, including: robustness of decision rules; simultaneous consideration of selflocation, motion planning, motion control and vehicle dynamics; the effect of sensor placement and attention focusing on sensor fusion; and adaptation of techniques from biological sensor fusion. I.
Asymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime
 ANN. STATIST
, 2004
"... An autoregressive process with Markov regime is an autoregressive process for which the regression function at each time point is given by a nonobservable Markov chain. In this paper we consider the asymptotic properties of the maximum likelihood estimator in a possibly nonstationary process of this ..."
Abstract

Cited by 34 (6 self)
 Add to MetaCart
An autoregressive process with Markov regime is an autoregressive process for which the regression function at each time point is given by a nonobservable Markov chain. In this paper we consider the asymptotic properties of the maximum likelihood estimator in a possibly nonstationary process of this kind for which the hidden state space is compact but not necessarily finite. Consistency and asymptotic normality are shown to follow from uniform exponential forgetting of the initial distribution for the hidden Markov chain conditional on the observations.
ConditionalMean Estimation Via JumpDiffusion Processes in Multiple Target Tracking/Recognition
 IEEE TRANSACTIONS ON SIGNAL PROCESSING
, 1995
"... A new algorithm is presented for generating the conditional mean estimates of functions of target positions, orientations and type in recognition, and tracking of an unknown nmnber of targets and target types. Taking a Bayesian approach, a posterior measure is defined on the tracking/target paramete ..."
Abstract

Cited by 28 (5 self)
 Add to MetaCart
A new algorithm is presented for generating the conditional mean estimates of functions of target positions, orientations and type in recognition, and tracking of an unknown nmnber of targets and target types. Taking a Bayesian approach, a posterior measure is defined on the tracking/target parameter space by combining a narrowband sensor array manifold model with a high resolution imaging model, and a prior based on airplane dynanfics. The Newtoninn force equations governing rigid body dynamic s are utilized to form the prior density on airplane motion. The conditional mean estimates are generated using a random sampling algorithm based on jumpdiffusion processes [1] i)r empirically generating MMSE estimates of functions of these random target positions, orientations, and type under the posterior measure. Results are presented on target tracking and identification from an implementation of the algorithm on a networked Silicon Graphics workstation and DECmpp/MasPar parallel machine.