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무인 운반체의 항법을 위한 위치, 자세 및 지도 추정 방법

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Author(s)
정준혁
Issued Date
2018
Keyword
EKF, 각속도 편차 보정, 센서 융합, IEKF SLAM, Graph-based SLAM
Abstract
This study implements estimation of location and attitude of unmanned vehicles and builds map of the environment. Extended Kalman filter is used for estimation of location and attitude, and graph-based optimization is utilized for the map building.
The extended Kalman filter is applied using bias-calibrated angular rate. The method estimates bias in angular rate measured by an Attitude Heading Reference System(AHRS). Also, it fuses multiple sensor measurements for localization of an underwater vehicle. The sensors used for the fusion are Inertial Measurement Unit(IMU), Short Baseline(SBL), Doppler Velocity Log(DVL), and depth meter.
Two methods are implemented for map building : Invariant Extended Kalman filter(IEKF) SLAM and Graph-based SLAM. IEKF SLAM is an on-line SLAM which results in robuster performance than EKF SLAM. The IEKF SLAM estimates the location of a robot as well as the location of landmarks. The Graph-based SLAM is an off-line algorithm which is a optimization approach based on graph representation of constraints. These methods are verified using open measurement data set and test-tank experiments.
Alternative Title
Estimation of Position, Attitude, and Map for Navigation of Unmanned Vehicles
Alternative Author(s)
Chung. Jun Hyuk
Affiliation
조선대학교 대학원 제어계측공학과
Department
일반대학원 제어계측공학과
Advisor
고낙용
Awarded Date
2019-02
Table Of Contents
제1장 서론 1
제2장 위치 및 자세 추정방법 4
제1절 각속도 편차 보정 방법을 사용한 EKF 4
제2절 센서 복합 항법 7
제3장 지도 추정 방법 11
제1절 IEKF SLAM 12
제2절 Graph-based SLAM 15
제4장 실험 및 시뮬레이션 18
제1절 각속도 편차 보정 방법을 사용한 EKF 18
1. 실험 환경 및 조건 18
2. 결과 20
제2절 센서 복합 항법 25
1. 실험 환경 및 조건 25
2. 결과 27
제3절 IEKF SLAM 31
1. 시뮬레이션 조건 31
2. 결과 32
제4절 Graph-based SLAM 34
1. 실험 환경 및 조건 34
2. 결과 35
제5장 결론 38
참고문헌 39
감사의 글 41
Degree
Master
Publisher
조선대학교 대학원
Citation
정준혁. (2018). 무인 운반체의 항법을 위한 위치, 자세 및 지도 추정 방법.
Type
Dissertation
URI
https://oak.chosun.ac.kr/handle/2020.oak/13764
http://chosun.dcollection.net/common/orgView/200000267166
Appears in Collections:
General Graduate School > 3. Theses(Master)
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