Imperial College London
Alternate

DrStefanLeutenegger

Faculty of Engineering, Department of Computing

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s.leutenegger Website

 
 
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ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Xu:2019:10.1109/ICRA.2019.8794371,
author = {Xu, B and Li, W and Tzoumanikas, D and Bloesch, M and Davison, A and Leutenegger, S},
doi = {10.1109/ICRA.2019.8794371},
publisher = {IEEE},
title = {MID-fusion: octree-based object-level multi-instance dynamic SLAM},
url = {http://dx.doi.org/10.1109/ICRA.2019.8794371},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We propose a new multi-instance dynamic RGB-D SLAM system using anobject-level octree-based volumetric representation. It can provide robustcamera tracking in dynamic environments and at the same time, continuouslyestimate geometric, semantic, and motion properties for arbitrary objects inthe scene. For each incoming frame, we perform instance segmentation to detectobjects and refine mask boundaries using geometric and motion information.Meanwhile, we estimate the pose of each existing moving object using anobject-oriented tracking method and robustly track the camera pose against thestatic scene. Based on the estimated camera pose and object poses, we associatesegmented masks with existing models and incrementally fuse correspondingcolour, depth, semantic, and foreground object probabilities into each objectmodel. In contrast to existing approaches, our system is the first system togenerate an object-level dynamic volumetric map from a single RGB-D camera,which can be used directly for robotic tasks. Our method can run at 2-3 Hz on aCPU, excluding the instance segmentation part. We demonstrate its effectivenessby quantitatively and qualitatively testing it on both synthetic and real-worldsequences.
AU  - Xu,B
AU  - Li,W
AU  - Tzoumanikas,D
AU  - Bloesch,M
AU  - Davison,A
AU  - Leutenegger,S
DO  - 10.1109/ICRA.2019.8794371
PB  - IEEE
PY  - 2019///
TI  - MID-fusion: octree-based object-level multi-instance dynamic SLAM
UR  - http://dx.doi.org/10.1109/ICRA.2019.8794371
UR  - http://arxiv.org/abs/1812.07976v4
UR  - http://hdl.handle.net/10044/1/71340
ER  -
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