|
|
|
|
| LEADER |
01000caa a22002652c 4500 |
| 001 |
NLM367058812 |
| 003 |
DE-627 |
| 005 |
20250305162444.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
240114s2024 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TVCG.2024.3353263
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n1223.xml
|
| 035 |
|
|
|a (DE-627)NLM367058812
|
| 035 |
|
|
|a (NLM)38215333
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Li, Jinyu
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a RD-VIO
|b Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments
|
| 264 |
|
1 |
|c 2024
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 05.09.2024
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a It is typically challenging for visual or visual-inertial odometry systems to handle the problems of dynamic scenes and pure rotation. In this work, we design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these two problems. First, we propose an IMU-PARSAC algorithm which can robustly detect and match keypoints in a two-stage process. In the first state, landmarks are matched with new keypoints using visual and IMU measurements. We collect statistical information from the matching and then guide the intra-keypoint matching in the second stage. Second, to handle the problem of pure rotation, we detect the motion type and adapt the deferred-triangulation technique during the data-association process. We make the pure-rotational frames into the special subframes. When solving the visual-inertial bundle adjustment, they provide additional constraints to the pure-rotational motion. We evaluate the proposed VIO system on public datasets and online comparison. Experiments show the proposed RD-VIO has obvious advantages over other methods in dynamic environments
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Pan, Xiaokun
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Huang, Gan
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zhang, Ziyang
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Wang, Nan
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Bao, Hujun
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zhang, Guofeng
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 30(2024), 10 vom: 12. Okt., Seite 6941-6955
|w (DE-627)NLM098269445
|x 1941-0506
|7 nnas
|
| 773 |
1 |
8 |
|g volume:30
|g year:2024
|g number:10
|g day:12
|g month:10
|g pages:6941-6955
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TVCG.2024.3353263
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 30
|j 2024
|e 10
|b 12
|c 10
|h 6941-6955
|