| LDR | | 02919nam u200457 4500 |
| 001 | | 000000418814 |
| 005 | | 20190215163155 |
| 008 | | 181129s2017 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438091016 |
| 035 | |
▼a (MiAaPQ)AAI10871321 |
| 035 | |
▼a (MiAaPQ)OhioLINK:osu1483669256597152 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 624 |
| 100 | 1 |
▼a Huai, Jianzhu. |
| 245 | 10 |
▼a Collaborative SLAM with Crowdsourced Data. |
| 260 | |
▼a [S.l.]:
▼b The Ohio State University.,
▼c 2017. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2017. |
| 300 | |
▼a 245 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B. |
| 500 | |
▼a Adviser: Dorota Grejner-Brzezinska. |
| 502 | 1 |
▼a Thesis (Ph.D.)--The Ohio State University, 2017. |
| 520 | |
▼a Navigation from one place to another involves estimating location and orientation. As such, navigation and location-based services are essential to our daily lives. Prevalent navigation techniques rely on integrating global navigation satellite |
| 520 | |
▼a This problem is approached at three aspects in this work: collaborative SLAM with crowdsourced visual data, calibrating a camera-IMU (inertial measurement unit) sensor system found on mobile devices, and collaborative SLAM with crowdsourced visu |
| 520 | |
▼a For the first aspect, a collaborative SLAM framework is proposed based on the client-server architecture. It has independent clients estimating the device motion using the visual data from mobile devices. The output from these clients is process |
| 520 | |
▼a For the second aspect, a calibration approach based on the extended Kalman filter (EKF) is developed for the camera-IMU system of a mobile device. Aimed at target-free on-the-fly calibration, this approach estimates the intrinsic parameters of b |
| 520 | |
▼a The calibration approach provides essential parameters for integrating visual and inertial data which is the highlight of the third aspect. There the collaborative SLAM framework is extended to work with visual and inertial data. While keeping s |
| 520 | |
▼a In summary, these research efforts have proved that accurate and effective collaborative SLAM is achievable with crowdsourced data at the level that has not been demonstrated before. It represents a step towards location-based services which har |
| 590 | |
▼a School code: 0168. |
| 650 | 4 |
▼a Civil engineering. |
| 650 | 4 |
▼a Computer engineering. |
| 690 | |
▼a 0543 |
| 690 | |
▼a 0464 |
| 710 | 20 |
▼a The Ohio State University.
▼b Civil Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-10B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0168 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2017 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000197
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |