Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 임종우 | - |
dc.date.accessioned | 2019-12-10T16:46:30Z | - |
dc.date.available | 2019-12-10T16:46:30Z | - |
dc.date.issued | 2018-12 | - |
dc.identifier.citation | SENSORS, v. 18, no. 12, Article no. 4287 | en_US |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://www.mdpi.com/1424-8220/18/12/4287 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/121053 | - |
dc.description.abstract | Visual-inertial odometry (VIO) has recently received much attention for efficient and accurate ego-motion estimation of unmanned aerial vehicle systems (UAVs). Recent studies have shown that optimization-based algorithms achieve typically high accuracy when given enough amount of information, but occasionally suffer from divergence when solving highly non-linear problems. Further, their performance significantly depends on the accuracy of the initialization of inertial measurement unit (IMU) parameters. In this paper, we propose a novel VIO algorithm of estimating the motional state of UAVs with high accuracy. The main technical contributions are the fusion of visual information and pre-integrated inertial measurements in a joint optimization framework and the stable initialization of scale and gravity using relative pose constraints. To account for the ambiguity and uncertainty of VIO initialization, a local scale parameter is adopted in the online optimization. Quantitative comparisons with the state-of-the-art algorithms on the European Robotics Challenge (EuRoC) dataset verify the efficacy and accuracy of the proposed method. | en_US |
dc.description.sponsorship | This research is supported by the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (NRF-2017M3C4A7069369), and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MISP) (NRF-2017R1A2B4011928). | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI | en_US |
dc.subject | visual-inertial odometry | en_US |
dc.subject | UAV navigation | en_US |
dc.subject | sensor fusion | en_US |
dc.subject | optimization | en_US |
dc.title | Visual-Inertial Odometry with Robust Initialization and Online Scale Estimation | en_US |
dc.type | Article | en_US |
dc.relation.no | 12 | - |
dc.relation.volume | 18 | - |
dc.identifier.doi | 10.3390/s18124287 | - |
dc.relation.page | 1-10 | - |
dc.relation.journal | SENSORS | - |
dc.contributor.googleauthor | Hong, Euntae | - |
dc.contributor.googleauthor | Lim, Jongwoo | - |
dc.relation.code | 2018007781 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF COMPUTER SCIENCE | - |
dc.identifier.pid | jlim | - |
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