Vision-aided terrain referenced navigation for unmanned aerial vehicles무인항공기 영상기반 지형참조항법 알고리듬 연구

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dc.contributor.advisorBang, Hyo-Choong-
dc.contributor.advisor방효충-
dc.contributor.authorLee, Dong-Jin-
dc.contributor.author이동진-
dc.date.accessioned2015-04-23T02:06:34Z-
dc.date.available2015-04-23T02:06:34Z-
dc.date.issued2013-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=566071&flag=dissertation-
dc.identifier.urihttp://hdl.handle.net/10203/196158-
dc.description학위논문(박사) - 한국과학기술원 : 항공우주공학전공, 2013.8, [ vii, 91 p. ]-
dc.description.abstractA vision-aided terrain referenced navigation (VATRN) approach is addressed for autonomous navigation of small unmanned aerial vehicles (UAVs) without GPS. A typical terrain referenced navigation (TRN) algorithm blends inertial navigation data with measured terrain information, then matches the data with the stored digital terrain elevation database (DTED) to estimate vehicle’s position. The accuracy and the quality of a TRN system rely on the performance of the inertial navigation system (INS). That is, low-cost UAVs that are not equipped with a high quality INS cannot obtain accurate estimation. For this reason, we supplement a low-cost INS with a vision system using a monocular camera. A point-mass filter based on Bayesian estimation is employed as a TRN algorithm. The point-mass filter is a quantized version of Bayesian filter and is applicable to nonlinear and non-Gaussian problems such as terrain referenced navigation. Moreover the point-mass filter is known as a robust algorithm against various terrain profiles and large initial errors. Homograpies are established to estimate the vehicle’s relative translational motion using ground features with simple assumptions. The feature points on the ground are detected and tracked by image processing algorithm and the outliers are eliminated using random sample consensus (RANSAC). Using singular value decomposition (SVD) of the constructed Homography matrix, the relative translation is calculated. The error analysis in homography estimation is explored to estimate the error covariance matrix associated with the visual odometry data. The error covariance property is changed by the altitude of a vehicle and attitude errors. The estimated error covariance is delivered to the TRN algorithm for robust estimation. Furthermore, multiple ground features tracked by image observations are utilized as multiple height measurements to improve the performance of the VATRN algorithm. This represents that the observability of te...eng
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectTerrain Referenced Navigation-
dc.subject호모그래피-
dc.subject무인항공기-
dc.subject영상기반항법-
dc.subject지형참조항법-
dc.subjectHomography Estimation-
dc.subjectVision-aided Navigation-
dc.subjectUnmanned Aerial Vehicle-
dc.titleVision-aided terrain referenced navigation for unmanned aerial vehicles-
dc.title.alternative무인항공기 영상기반 지형참조항법 알고리듬 연구-
dc.typeThesis(Ph.D)-
dc.identifier.CNRN566071/325007 -
dc.description.department한국과학기술원 : 항공우주공학전공, -
dc.identifier.uid020085133-
dc.contributor.localauthorBang, Hyo-Choong-
dc.contributor.localauthor방효충-
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