C
Christian Rauch
Researcher at University of Edinburgh
Publications - 8
Citations - 56
Christian Rauch is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Robot & Articulated robot. The author has an hindex of 4, co-authored 8 publications receiving 33 citations. Previous affiliations of Christian Rauch include University of Bremen.
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Journal ArticleDOI
ART-based fusion of multi-modal perception for robots
TL;DR: A new fusion strategy which is very flexible and therefore can be easily adapted to new domains or sensor configurations which is based on the Adaptive Resonance Theory (ART) and is inherently capable of incremental on-line learning.
Journal ArticleDOI
RigidFusion: Robot Localisation and Mapping in Environments With Large Dynamic Rigid Objects
TL;DR: In this paper, an RGB-D SLAM approach is proposed to simultaneously segment, track and reconstruct the static background and large dynamic rigid objects that can occlude major portions of the camera view.
Journal ArticleDOI
Cognitive AutonomouS CAtheters Operating in Dynamic Environments
Emmanuel Vander Poorten,Phuong Toan Tran,Alain Devreker,Caspar Gruijthuijsen,Sergio Portoles Diez,Gabrijel Smoljkic,Vule Strbac,Nele Famaey,Dominiek Reynaerts,Jos Vander Sloten,Abraham Temesgen Tibebu,Bingbin Yu,Christian Rauch,Felix Bernard,Yohannes Kassahun,Jan Hendrik Metzen,Stamatia Giannarou,Liang Zhao,Su-Lin Lee,Guang-Zhong Yang,Evangelos B. Mazomenos,Ping-Lin Chang,Danail Stoyanov,Maryna Kvasnytsia,Joris Van Deun,Eva Verhoelst,Mauro Sette,Anita Di Iasio,Giovanni Leo,Fabian Hertner,Daniel Scherly,Leandro Chelini,Nicolai Häni,Dejan Seatovic,Benoit Rosa,Herbert De Praetere,Paul Herijgers +36 more
TL;DR: Progress is reported on the development of the necessary technology to autonomously steer catheters through the vasculature in terms of catheter design, vessel reconstruction, catheter shape modeling, surgical skill analysis, decision making and control.
Proceedings ArticleDOI
Visual Articulated Tracking in the Presence of Occlusions
TL;DR: This paper proposes to use the per-pixel data-to-model associations provided from a random forest to avoid local minima during model fitting by training the random forest with artificial occlusions to achieve increased robustness to occlusion and clutter present in the scene.
Posted Content
RigidFusion: Robot Localisation and Mapping in Environments with Large Dynamic Rigid Objects
TL;DR: This work presents a novel RGB-D SLAM approach to simultaneously segment, track and reconstruct the static background and large dynamic rigid objects that can occlude major portions of the camera view in environments where dynamic objects cause large occlusion.