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Towards identification of best practice algorithms in 3D perception and modeling
urn:nbn:de:0011-n-1849815 (140 KByte PDF)
MD5 Fingerprint: 106507d23487234c35104d41ef31beb4
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Created on: 19.11.2011
|IEEE Robotics and Automation Society; Shanghai Jiao Tong University:|
IEEE International Conference on Robotics and Automation, ICRA 2011. DVD : Better Robots, Better Life. May 9-13, 2011, Shanghai, China; Workshop Semantic Perception, Mapping and Exploration (SPME)
New York, NY: IEEE, 2011
|International Conference on Robotics and Automation (ICRA) <2011, Shanghai>|
Workshop Semantic Perception, Mapping and Exploration (SPME) <2011, Shanghai>
| Conference Paper, Electronic Publication|
|Fraunhofer IPA ()|
| 3D-Bildverarbeitung; Best Practice; Navigation; Algorithmus; Bildverarbeitung; Objekterkennung; Software|
Robots need a representation of their environment to reason about and to interact with it. Different 3D perception and modeling approaches exist to create such a representation, but they are not yet easily comparable. This work tries to identify best practice algorithms in the domain of 3D perception and modeling with a focus on environment reconstruction for robotic applications. The goal is to have a collection of refactored algorithms that are easily measurable and comparable. The realization follows a methodology consisting of five steps. After a survey of relevant algorithms and libraries, common representations for the core data-types Cartesian point, Cartesian point cloud and triangle mesh are identified for use in harmonized interfaces. Atomic algorithms are encapsulated into four software components: the Octree component, the Iterative Closest Point component, the k-Nearest Neighbors search component and the Delaunay triangulation component. A sample experimentdemonstrates how the component structure can be used to deduce best practice.