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논문 기본 정보

자료유형
학위논문
저자정보

윤인도 (서울대학교, 서울대학교 대학원)

발행연도
2018
저작권
서울대학교 논문은 저작권에 의해 보호받습니다.

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This thesis compares and analyzes a performance of template matching based terrain referenced navigation (TMTRN) using correlation functions according to different error types and correlation functions. Conventional batch processing TRN generally utilizes the radar altimeter and adopts mean square difference (MSD), mean absolute difference (MAD), and normalized cross correlation (NCC) for matching a batch profile with terrain database. If a flash LiDAR is utilized instead of the radar, it is possible to build a profile in one-shot. A point cloud of the flash LiDAR can be transformed into 2D profile, unlike a vector profile obtained from batch processing. Therefore, by using the flash LiDAR we can apply new correlation functions such as image Euclidean distance (IMED) and image normalized cross correlation (IMNCC) which have been used in computer vision field. The simulation result shows that IMED is the most robust for different types of errors.

목차

Chapter 1 Introduction 1
1.1 Motivation and background . . . . . . . . . . . . . . . . . . . . . 1
1.2 Objectives and contributions . . . . . . . . . . . . . . . . . . . . 3
Chapter 2 Related Works 5
2.1 Terrain Referenced Navigation . . . . . . . . . . . . . . . . . . . 5
2.1.1 LiDAR-based TRN . . . . . . . . . . . . . . . . . . . . . . 10
2.1.2 Image-based TRN . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Template Matching . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.1 General idea of template matching . . . . . . . . . . . . . 16
2.2.2 Correlation function . . . . . . . . . . . . . . . . . . . . . 17
Chapter 3 Template matching based TRN 22
3.1 Relationship with BPTRN . . . . . . . . . . . . . . . . . . . . . . 22
3.2 TMTRN algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Chapter 4 Simulation Results 30
4.1 Template matching of terrain PC . . . . . . . . . . . . . . . . . . 30
4.2 TMTRN simulation . . . . . . . . . . . . . . . . . . . . . . . . . 33
Chapter 5 Conclusions and Future Works 46
5.1 Summary of the contribution . . . . . . . . . . . . . . . . . . . . 46
5.2 Future works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

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