DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 심현철 | - |
dc.contributor.author | Nam, Hyunwoo | - |
dc.contributor.author | 남현우 | - |
dc.date.accessioned | 2024-07-30T19:30:51Z | - |
dc.date.available | 2024-07-30T19:30:51Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1096202&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/321417 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 로봇공학학제전공, 2024.2,[v, 35p. :] | - |
dc.description.abstract | Autonomous racing competitions, With the advancement of autonomous driving technology, have emerged highlights the importance of precision localization. The autonomous challenges have held in open-sky environments, where GPS-based approaches have powerful advantages. However, to enhance scalability across diverse conditions and ensure system reliability that is not compromised by single-sensor failures, heterogeneous sensor integration for global positioning is essential. LiDAR, in particular, is vital for its rich geometric information, contributing to the sophistication of autonomous driving systems. Nonetheless, adopting LiDAR comes with the challenge of processing vast amounts of data it generates. This research introduces a framework to elevate the scalability and reliability of high-speed autonomous systems by refining LiDAR map matching. Techniques like ground extraction significantly reduce computational load by approximately 40%, while addressing LiDAR sensor distortions and time-delay compensation, crucial in high-speed settings. Lastly, the research presents a graph optimization method to create georeferenced point cloud maps essential for map matching, combining LiDAR data with INS. The effectiveness of framework was validated using two distinct datasets acquired from real-world environments. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 자율 주행▼a자율 주행 경주▼a위치 추정▼a지도 매칭▼a그래프 최적화 | - |
dc.subject | Autonomous driving▼aAutonomous racing▼aLocalization▼aMap matching▼aGraph optimization | - |
dc.title | Light-weighted and accurate map matching localization for high-speed autonomous racing | - |
dc.title.alternative | 고속 자율 주행을 위한 지도 매칭 기반 위치 추정의 경량화 및 정확도 향상 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :로봇공학학제전공, | - |
dc.contributor.alternativeauthor | Shim, Hyunchul | - |
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