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GP-BayesFilters:贝叶斯滤波高斯过程回归

GP-BayesFilters: Gaussian Process Regression for Bayesian Filtering
课程网址: http://videolectures.net/nipsworkshops09_fox_gprbf/  
主讲教师: Dieter Fox
开课单位: 华盛顿大学
开课时间: 2010-01-19
课程语种: 英语
中文简介:
贝叶斯滤波的递归估计从传感器数据流的动态系统的状态。每个贝叶斯过滤器的主要组成部分是概率预报和观测模型。在机器人技术,这些模型通常是基于物理过程产生的数据的参数化描述。在这个演讲中我将说明如何非参数高斯过程的预测和观测模型,可以集成到贝叶斯过滤器的不同版本,即粒子滤波器和扩展和无迹卡尔曼滤波器。由此产生的GP-BayesFilters可以有几个优点超过标准的过滤器。最重要的是,GP-BayesFilters并不需要一个准确的,参数化模型的系统。如果有足够的训练数据,从而提高跟踪精度比参数化模型,并适度地降低模型的不确定性增加。我们的火车从部分或完全未标记的训练数据的GP-BayesFilters扩展高斯过程潜变量模型。该技术进行评估的背景下,视觉跟踪的微型飞艇和IMU基于这跟踪  
课程简介: Bayes filters recursively estimate the state of dynamical systems from streams of sensor data. Key components of each Bayes filter are probabilistic prediction and observation models. In robotics, these models are typically based on parametric descriptions of the physical process generating the data. In this talk I will show how non-parametric Gaussian process prediction and observation models can be integrated into different versions of Bayes filters, namely particle filters and extended and unscented Kalman filters. The resulting GP-BayesFilters can have several advantages over standard filters. Most importantly, GP-BayesFilters do not require an accurate, parametric model of the system. Given enough training data, they enable improved tracking accuracy compared to parametric models, and they degrade gracefully with increased model uncertainty. We extend Gaussian Process Latent Variable Models to train GP-BayesFilters from partially or fully unlabeled training data. The techniques are evaluated in the context of visual tracking of a micro blimp and IMU-based tracking of a slotcar.
关 键 词: 贝叶斯过滤器; 数据流; 粒子滤波器
课程来源: 视频讲座网公开课
入库时间: 2013-09-02
最后编审: 2020-05-30:王勇彬(课程编辑志愿者)
阅读次数: 707