Imperial College London
Alternate

ProfessorAndrewDavison

Faculty of Engineering, Department of Computing

Professor of Robot Vision
 
 
 
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Contact

 

+44 (0)20 7594 8316a.davison Website

 
 
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Assistant

 

Ms Lucy Atthis +44 (0)20 7594 8259

 
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Location

 

303William Penney LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Bloesch:2020:10.1109/iccv.2019.00595,
author = {Bloesch, M and Laidlow, T and Clark, R and Leutenegger, S and Davison, A},
doi = {10.1109/iccv.2019.00595},
publisher = {IEEE},
title = {Learning meshes for dense visual SLAM},
url = {http://dx.doi.org/10.1109/iccv.2019.00595},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Estimating motion and surrounding geometry of a moving camera remains a challenging inference problem. From an information theoretic point of view, estimates should get better as more information is included, such as is done in dense SLAM, but this is strongly dependent on the validity of the underlying models. In the present paper, we use triangular meshes as both compact and dense geometry representation. To allow for simple and fast usage, we propose a view-based formulation for which we predict the in-plane vertex coordinates directly from images and then employ the remaining vertex depth components as free variables. Flexible and continuous integration of information is achieved through the use of a residual based inference technique. This so-called factor graph encodes all information as mapping from free variables to residuals, the squared sum of which is minimised during inference. We propose the use of different types of learnable residuals, which are trained end-to-end to increase their suitability as information bearing models and to enable accurate and reliable estimation. Detailed evaluation of all components is provided on both synthetic and real data which confirms the practicability of the presented approach.
AU  - Bloesch,M
AU  - Laidlow,T
AU  - Clark,R
AU  - Leutenegger,S
AU  - Davison,A
DO  - 10.1109/iccv.2019.00595
PB  - IEEE
PY  - 2020///
TI  - Learning meshes for dense visual SLAM
UR  - http://dx.doi.org/10.1109/iccv.2019.00595
UR  - https://ieeexplore.ieee.org/document/9009776
UR  - http://hdl.handle.net/10044/1/77846
ER  -
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