Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
160,119
Rao-blackwellised phd slam
- In 2010 IEEE Int. Conf. on Robotics and Automation
, 2010
"... Abstract — This paper proposes a tractable solution to feature-based (FB) SLAM in the presence of data association uncertainty and uncertainty in the number of features. By modeling the feature map as a random finite set (RFS), a rigorous Bayesian formulation of the FB-SLAM problem that accounts for ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
the posterior PHD or intensity function of the map and the posterior distribution of the trajectory of the vehicle. A Rao-Blackwellised implementation of the PHD-SLAM filter is proposed based on the Gaussian mixture PHD filter for the map and the particle filter for the vehicle trajectory. Simulated results
Rao-Blackwellised Particle Filtering via Data Augmentation
- In Advances in Neural Information Processing Systems
, 2001
"... In this paper, we extend the Rao-Blackwellised particle filtering method to more complex hybrid models consisting of Gaussian latent variables and discrete observations. This is accomplished by augmenting the models with artificial variables that enable us to apply Rao-Blackwellisation. Other improv ..."
Abstract
-
Cited by 13 (2 self)
- Add to MetaCart
In this paper, we extend the Rao-Blackwellised particle filtering method to more complex hybrid models consisting of Gaussian latent variables and discrete observations. This is accomplished by augmenting the models with artificial variables that enable us to apply Rao-Blackwellisation. Other
Rao-Blackwellised Particle Filter for Tracking with Application
- in Visual Surveillance”, the Second Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), in conjunction with ICCV 2005, Oct
"... Particle filters have become popular tools for visual tracking since they do not require the modeling system to be Gaussian and linear. However, when applied to a high dimensional state-space, particle filters can be inefficient because a prohibitively large number of samples may be required in orde ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
in order to approximate the underlying density functions with desired accuracy. In this paper, by proposing a tracking algorithm based on Rao-Blackwellised particle filter (RBPF), we show how to exploit the analytical relationship between state variables to improve the efficiency and accuracy of a regular
Exact Approximation of Rao-Blackwellised Particle Filters?
"... for performing inference in non-linear non-Gaussian state-space models. The class of “Rao-Blackwellised ” particle filters exploits the analytic marginalisation that is possible for some state-space models to reduce the variance of the Monte Carlo estimates. Despite being applicable to only a restri ..."
Abstract
-
Cited by 2 (1 self)
- Add to MetaCart
for performing inference in non-linear non-Gaussian state-space models. The class of “Rao-Blackwellised ” particle filters exploits the analytic marginalisation that is possible for some state-space models to reduce the variance of the Monte Carlo estimates. Despite being applicable to only a
Rao-Blackwellised 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 Rao-Blackwellised particle filtering. In this setting, there is one different linearGaussian state space model for each possible discrete state of operation. The task of ..."
Abstract
-
Cited by 60 (1 self)
- Add to MetaCart
We tackle the fault diagnosis problem using conditionally Gaussian state space models and an efficient Monte Carlo method known as Rao-Blackwellised particle filtering. In this setting, there is one different linearGaussian state space model for each possible discrete state of operation. The task
Particle swarm optimization
, 1995
"... eberhart @ engr.iupui.edu A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications ..."
Abstract
-
Cited by 3535 (22 self)
- Add to MetaCart
eberhart @ engr.iupui.edu A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described
Vision-based SLAM using the Rao-Blackwellised Particle Filter [p. 9]
, 2005
"... workshops (four of them at IJCAI), bringing together researchers from the AI and the robotics community to discuss issues related to robotics and artificial intelligence, and in particular on ways to deal with uncertainty. This year RUR-05 has focussed on the following topics: – Representations for ..."
Abstract
- Add to MetaCart
workshops (four of them at IJCAI), bringing together researchers from the AI and the robotics community to discuss issues related to robotics and artificial intelligence, and in particular on ways to deal with uncertainty. This year RUR-05 has focussed on the following topics: – Representations for dealing with uncertainty in robotics. – Planning in continuous state/action/observation spaces. – Simultaneous localization and map building (SLAM). – Single- and multi-robot planning under motion and sensor noise. – Simultaneous planning, localization, and map building (SPLAM). – Successful applications. We would like to thank the authors for their interest in the workshop, as well as the members of
Results 1 - 10
of
160,119
