Location: EU Supported » PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning » Regression in Robotics - Approaches and Applications

Gaussian processes for Bayesian Filtering

author:Dieter Fox, Department of Computer Science and Engineering, University of Washington
published: Aug. 3, 2009,   recorded: June 2009,   views: 87
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Slides

Slides
0:00 Gaussian Processes for Bayesian Filtering
0:38 Bayesian Filtering
2:37 Bayes Filters
3:37 Observation and Dynamics Models
5:00 Dynamics and Observation Models
6:43 GP-BayesFilters
7:18 Gaussian Process Models
8:19 GP Setting
10:01 Functions Sampled from Priors
10:22 GP Prediction
10:55 1D Gaussian Process Example
11:19 Kernel Width
13:40 Hyperparameter Estimation
14:08 WiFi Sensor Model
16:25 Location Model
16:42 Tracking Example
17:53 GP - BayesFilters
21:30 Blimp Tracking
21:41 GP - UKF Tracking Example
22:13 Blimp Results
23:54 Dealing with Training Data Sparsity
24:35 Going Latent
25:32 WiFi - SLAM
25:41 GPDM: Latent Variable Models for Dynamical Systems
26:03 GPBF - Learning
26:37 Slotcar Tracking
26:49 Data
27:20 Learned 2D Embedding
28:07 Time Alignment
28:39 Beyond Plain GP Regression
30:06 Summary

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