Abstract
This paper proposes a vision-only online mosaicing method for underwater surveys. Our method tackles a common problem in low-cost imaging platforms, where complementary navigation sensors produce imprecise or even missing measurements. Under these circumstances, the success of the optical mapping depends on the continuity of the acquired video stream. However, this continuity cannot be always guaranteed due to the motion blurs or lack of texture, common in underwater scenarios. Such temporal gaps hinder the extraction of reliable motion estimates from visual odometry, and compromise the ability to infer the presence of loops for producing an adequate optical map. Unlike traditional underwater mosaicing methods, our proposal can handle camera trajectories with gaps between time-consecutive images. This is achieved by constructing minimum spanning tree which verifies whether the current topology is connected or not. To do so, we embed a trajectory estimate correction step based on graph theory algorithms. The proposed method was tested with several different underwater image sequences and results were presented to illustrate the performance.
| Original language | English |
|---|---|
| Pages (from-to) | 217-229 |
| Number of pages | 13 |
| Journal | Intelligent Service Robotics |
| Volume | 9 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2016 Jul 1 |
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All Science Journal Classification (ASJC) codes
- Computational Mechanics
- Engineering (miscellaneous)
- Mechanical Engineering
- Artificial Intelligence
Cite this
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Online underwater optical mapping for trajectories with gaps. / Elibol, Armagan; Shim, Hyunjung; Hong, Seonghun; Kim, Jinwhan; Gracias, Nuno; Garcia, Rafael.
In: Intelligent Service Robotics, Vol. 9, No. 3, 01.07.2016, p. 217-229.Research output: Contribution to journal › Article
TY - JOUR
T1 - Online underwater optical mapping for trajectories with gaps
AU - Elibol, Armagan
AU - Shim, Hyunjung
AU - Hong, Seonghun
AU - Kim, Jinwhan
AU - Gracias, Nuno
AU - Garcia, Rafael
PY - 2016/7/1
Y1 - 2016/7/1
N2 - This paper proposes a vision-only online mosaicing method for underwater surveys. Our method tackles a common problem in low-cost imaging platforms, where complementary navigation sensors produce imprecise or even missing measurements. Under these circumstances, the success of the optical mapping depends on the continuity of the acquired video stream. However, this continuity cannot be always guaranteed due to the motion blurs or lack of texture, common in underwater scenarios. Such temporal gaps hinder the extraction of reliable motion estimates from visual odometry, and compromise the ability to infer the presence of loops for producing an adequate optical map. Unlike traditional underwater mosaicing methods, our proposal can handle camera trajectories with gaps between time-consecutive images. This is achieved by constructing minimum spanning tree which verifies whether the current topology is connected or not. To do so, we embed a trajectory estimate correction step based on graph theory algorithms. The proposed method was tested with several different underwater image sequences and results were presented to illustrate the performance.
AB - This paper proposes a vision-only online mosaicing method for underwater surveys. Our method tackles a common problem in low-cost imaging platforms, where complementary navigation sensors produce imprecise or even missing measurements. Under these circumstances, the success of the optical mapping depends on the continuity of the acquired video stream. However, this continuity cannot be always guaranteed due to the motion blurs or lack of texture, common in underwater scenarios. Such temporal gaps hinder the extraction of reliable motion estimates from visual odometry, and compromise the ability to infer the presence of loops for producing an adequate optical map. Unlike traditional underwater mosaicing methods, our proposal can handle camera trajectories with gaps between time-consecutive images. This is achieved by constructing minimum spanning tree which verifies whether the current topology is connected or not. To do so, we embed a trajectory estimate correction step based on graph theory algorithms. The proposed method was tested with several different underwater image sequences and results were presented to illustrate the performance.
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U2 - 10.1007/s11370-016-0195-4
DO - 10.1007/s11370-016-0195-4
M3 - Article
AN - SCOPUS:84961644886
VL - 9
SP - 217
EP - 229
JO - Intelligent Service Robotics
JF - Intelligent Service Robotics
SN - 1861-2776
IS - 3
ER -