Details
| Original language | English |
|---|---|
| Pages (from-to) | 350-357 |
| Number of pages | 8 |
| Journal | IEEE Robotics and Automation Letters |
| Volume | 7 |
| Issue number | 1 |
| Early online date | 9 Nov 2021 |
| Publication status | Published - 1 Jan 2022 |
Abstract
This letter presents a robust, real-time visual odometry that efficiently exploits the available visual and geometry cues from RGB-D frames for both tracking and mapping. Together with a hybrid tracking algorithm based on a joint multi-objective formulation, we additionally incorporate the point-to-plane metrics into the photometric bundle adjustment (PBA) to constrain the iteration direction especially for those weakly-textured points. The relative pose constraints derived from optimized poses via PBA is then leveraged in combination with reprojection constraints retrieved from maintained feature map, to refine keyframe poses and feature locations. Moreover, the slanted support plane commonly used in multi-view stereo matching, is utilized for the adjustment of the semi-dense points to further enhance mapping accuracy that in turn benefits the front-end tracking. We extensively evaluate our algorithm on benchmark datasets, and those experimental results validate the advantage of our method in terms of overall tracking performance over other representative approaches.
Keywords
- mapping, RGB-D images, Visual odometry
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Biomedical Engineering
- Computer Science(all)
- Human-Computer Interaction
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Control and Optimization
- Computer Science(all)
- Artificial Intelligence
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In: IEEE Robotics and Automation Letters, Vol. 7, No. 1, 01.01.2022, p. 350-357.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Robust RGBD Visual Odometry Using Windowed Direct Bundle Adjustment and Slanted Support Plane
AU - Luo, Hang
AU - Pape, Christian
AU - Reithmeier, Eduard
PY - 2022/1/1
Y1 - 2022/1/1
N2 - This letter presents a robust, real-time visual odometry that efficiently exploits the available visual and geometry cues from RGB-D frames for both tracking and mapping. Together with a hybrid tracking algorithm based on a joint multi-objective formulation, we additionally incorporate the point-to-plane metrics into the photometric bundle adjustment (PBA) to constrain the iteration direction especially for those weakly-textured points. The relative pose constraints derived from optimized poses via PBA is then leveraged in combination with reprojection constraints retrieved from maintained feature map, to refine keyframe poses and feature locations. Moreover, the slanted support plane commonly used in multi-view stereo matching, is utilized for the adjustment of the semi-dense points to further enhance mapping accuracy that in turn benefits the front-end tracking. We extensively evaluate our algorithm on benchmark datasets, and those experimental results validate the advantage of our method in terms of overall tracking performance over other representative approaches.
AB - This letter presents a robust, real-time visual odometry that efficiently exploits the available visual and geometry cues from RGB-D frames for both tracking and mapping. Together with a hybrid tracking algorithm based on a joint multi-objective formulation, we additionally incorporate the point-to-plane metrics into the photometric bundle adjustment (PBA) to constrain the iteration direction especially for those weakly-textured points. The relative pose constraints derived from optimized poses via PBA is then leveraged in combination with reprojection constraints retrieved from maintained feature map, to refine keyframe poses and feature locations. Moreover, the slanted support plane commonly used in multi-view stereo matching, is utilized for the adjustment of the semi-dense points to further enhance mapping accuracy that in turn benefits the front-end tracking. We extensively evaluate our algorithm on benchmark datasets, and those experimental results validate the advantage of our method in terms of overall tracking performance over other representative approaches.
KW - mapping
KW - RGB-D images
KW - Visual odometry
UR - http://www.scopus.com/inward/record.url?scp=85120424457&partnerID=8YFLogxK
U2 - 10.1109/LRA.2021.3126347
DO - 10.1109/LRA.2021.3126347
M3 - Article
AN - SCOPUS:85120424457
VL - 7
SP - 350
EP - 357
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
SN - 2377-3766
IS - 1
ER -