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Probabilistic Estimation of Incomplete Map Using Gaussian Process
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 오성회 | - |
| dc.contributor.author | 레너드 박 | - |
| dc.date.accessioned | 2017-07-14T03:03:38Z | - |
| dc.date.available | 2017-07-14T03:03:38Z | - |
| dc.date.issued | 2013-08 | - |
| dc.identifier.other | 000000012664 | - |
| dc.identifier.uri | https://hdl.handle.net/10371/123226 | - |
| dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 전기공학부, 2013. 8. 오성회. | - |
| dc.description.abstract | Gaussian process is a powerful probabilistic estimation tool which is used widely in engineering fields such as Computer vision, Robotics and sensor networks, etc. This thesis implemented an estimation algorithm of the total map with sparse sensing data using Gaussian Process. In the implemented algorithm, two kinds of kernel functions are applied to the spatial Gaussian Process model | - |
| dc.description.abstract | squared exponential kernel and neural network kernel. The performance of the proposed algorithm was verified by the experiments with a simple mobile sensor network. To construct a simple mobile sensor network based on ROS (Robot Operating System) platform, a two wheeled mobile robot (Pioneer3DX) and a two dimensional laser scanner (SICKlms200) are used. | - |
| dc.description.tableofcontents | 1 Introduction 1
1.1 Mobile Sensor Network 1.2 Simulataneous Localization and Mapping (SLAM) 1.3 Occupancy Grid Map 2 Related Work 2.1 Mapping 2.2 Sensing and Locating 2.3 Probabilistic Solution for Mapping Problem 3 Gaussian Process (GP) 3.1 Weight-space View 3.1.1 The Standard Linear Model 3.1.2 Projections of Inputs into Feature Space 3.2 Function-space View 3.2.1 Prediction with Noise-free Observations 3.2.2 Prediction using Noisy Observations 3.3 Varying Hyperparameters 4 GP Applied to Mapping Problem 4.1 Overview of Contextual Mapping 4.2 Training Hyperparameters 5 Experimental results 5.1 The Mapping Problem in a Real Indoor Environment 5.2 GP Estimation for Single Frame of Laser Scanner 5.2.1 The given Training Data 5.2.2 Selecting a Kernel Function 5.2.3 Optimizing Hyper-parameters 5.2.4 Estimation 5.3 The Estimation Problem in a Simulated Environment 6 Conclusion 6.1 Contribution of GP for Mapping Problem 6.2 Future Works | - |
| dc.format | application/pdf | - |
| dc.format.extent | 1510128 bytes | - |
| dc.format.medium | application/pdf | - |
| dc.language.iso | en | - |
| dc.publisher | 서울대학교 대학원 | - |
| dc.subject | Gaussian Process | - |
| dc.subject | Mobile Sensor Network | - |
| dc.subject | Estimation of map. Kernel | - |
| dc.subject.ddc | 621 | - |
| dc.title | Probabilistic Estimation of Incomplete Map Using Gaussian Process | - |
| dc.type | Thesis | - |
| dc.description.degree | Master | - |
| dc.citation.pages | 49 | - |
| dc.contributor.affiliation | 공과대학 전기공학부 | - |
| dc.date.awarded | 2013-08 | - |
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