GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models

Ko, Jonathan; Fox, Dieter

doi:10.1007/s10514-009-9119-x

Cited by 277 publications

(178 citation statements)

References 28 publications

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“…Each conditional distribution over a target variable is Gaussian, which favours their application in the context of recursive Bayes Filters. Ko and Fox (2009) use GPs for Bayesian filtering (GPBayes Filter), emphasising that in this way parametric prediction and parametric observation models can be avoided. For the prediction of a robot's state transition, the authors define a Gaussian Process taking as input previous state and control sequences of the robot.…”

Section: Related Workmentioning

confidence: 99%

A Gaussian Process Based Multi-Person Interaction Model

Klinger¹,

Rottensteiner²,

Heipke³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Online multi-person tracking in image sequences is commonly guided by recursive filters, whose predictive models define the expected positions of future states. When a predictive model deviates too much from the true motion of a pedestrian, which is often the case in crowded scenes due to unpredicted accelerations, the data association is prone to fail. In this paper we propose a novel predictive model on the basis of Gaussian Process Regression. The model takes into account the motion of every tracked pedestrian in the scene and the prediction is executed with respect to the velocities of all interrelated persons. As shown by the experiments, the model is capable of yielding more plausible predictions even in the presence of mutual occlusions or missing measurements. The approach is evaluated on a publicly available benchmark and outperforms other state-of-the-art trackers.

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Section: Related Workmentioning

confidence: 99%

A Gaussian Process Based Multi-Person Interaction Model

Klinger¹,

Rottensteiner²,

Heipke³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…A good introduction into GPs can be found in [17]. In robotics, GPs have been used for terrain modeling [20], for occupancy mapping [16], for estimating gas distributions [19], learning motion and observation models [12] and several other problems. In some parts, the approach of Vasudevan et al [20] is similar to our method.…”

Section: Related Workmentioning

confidence: 99%

Efficient motion planning for manipulation robots in environments with deformable objects

Frank¹,

Stachniss²,

Abdo³

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-The ability to plan their own motions and to reliably execute them is an important precondition for most autonomous robots. In this paper, we consider the problem of planning the motion of a mobile manipulation robot in the context of deformable objects in the environment. Our approach combines probabilistic roadmap planning with a deformation simulation system. Since appropriate physical deformation simulation is computationally demanding, we use an efficient variant of Gaussian Process regression to estimate the deformation cost for individual objects based on training examples. We generate the training data as a preprocessing step offline using the physical deformation simulation system so that no simulations are needed during runtime. We implemented and tested our approach on a mobile manipulation robot. Our experiments show that the robot is able to accurately predict and thus consider the deformation cost its manipulator introduces to the environment during motion planning. Simultaneously, the computation time is substantially reduced compared to the system that employs physical simulations online.

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“…Within this framework, it is possible to have efficient filtering using a gaussian distribution over a space of gaussian probability laws, allowing to handle directly continuous variables. In [31], the authors show how to do efficient state estimation with a batch learned gaussian process. In [32], they extend this framework to learn gaussian processes with hidden variables that allow to filter sensor noise very efficiently.…”

Section: Learning Probabilistic Models Of a Dynamic Systemmentioning

confidence: 99%

Learning the behavior model of a robot

2010

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Complex artifacts are designed today from well specified and well modeled components. But most often, the models of these components cannot be composed into a global functional model of the artifact. A significant observation, modeling and identification effort is required to get such a global model, which is needed in order to better understand, control and improve the designed artifact.Robotics provides a good illustration of this need. Autonomous robots are able to achieve more and more complex tasks, relying on more advanced sensori-motor functions. To better understand their behavior and improve their performance, it becomes necessary but more difficult to characterize and to model, at the global level, how robots behave in a given environment. Low-level models of sensors, actuators and controllers cannot be easily combined into a behavior model. Sometimes high level models operators used for planning are also available, but generally they are too coarse to represent the actual robot behavior.We propose here a general framework for learning from observation data the behavior model of a robot when performing a given task. The behavior is modeled as a Dynamic Bayesian Network, a convenient stochastic structured representations. We show how such a probabilistic model can be learned and how it can be used to improve, on line, the robot behavior with respect to a specific environment and user preferences. Framework and algorithms are detailed; they are substantiated by experimental results for autonomous navigation tasks.

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GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models

Cited by 277 publications

References 28 publications

A Gaussian Process Based Multi-Person Interaction Model

A Gaussian Process Based Multi-Person Interaction Model

Efficient motion planning for manipulation robots in environments with deformable objects

Learning the behavior model of a robot

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