Opti-acoustic underwater SLAM using style-transferred multi-modal matching스타일 변환을 이용한 옵티어쿠스틱 기반의 수중 로봇 SLAM

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Underwater robot pose and odometry estimation has constraints that have to solve challenging sensor modalities. In this thesis, I propose the underwater vehicle odometry optimization and estimation method with neural network and opti-acoustic-inertial module configuration. Neural network-based style transfer enables feature detection and matching between optic and sonar images. Feature points are utilized for deriving the reprojection errors and extrinsic matrix between the sonar and camera. Robot pose estimation is accomplished with error factor minimization, and optimize the odometry with pose factor inserted graph simultaneous localization and mapping (SLAM). Also learning-based selective link proposal facilitates efficient SLAM processing. The complications came from sensor modality are overcame with style transfer method and opti-acoustic-inertial module configuration.
Advisors
Kim, Ayoungresearcher김아영researcher
Description
한국과학기술원 :건설및환경공학과,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 건설및환경공학과, 2020.8,[iv, 36 p. :]

Keywords

Opti-acoustics▼aUnderwater robot▼aStyle transfer▼aDeep learning▼aGraph SLAM; 광-음향학▼a수중 로봇▼a스타일 변환▼a딥러닝▼a그래프 슬램

URI
http://hdl.handle.net/10203/285018
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=925179&flag=dissertation
Appears in Collection
CE-Theses_Master(석사논문)
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