Name
Abstract | ||
|---|---|---|
This work introduces a novel solution for localizing objects based on search strings and freely available Google SketchUp models. To this end we automatically download and preprocess a collection of 3D models to obtain equivalent point clouds. The outdoor scan is segmented into individual objects, which are sequentially matched with the models by a variant of iterative closest points algorithm using seven degrees of freedom and resulting in a highly precise pose estimation of the object. An error function evaluates the similarity level. The approach is verified using various segmented cars and their corresponding 3D models. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICAT.2011.6102106 | 2011 XXIII International Symposium on Information, Communication and Automation Technologies |
Keywords | Field | DocType |
object localization,3D model,Google SketchUp,iterative closest points algorithm with scale,3D laser scan | String searching algorithm,Computer vision,Error function,Computer science,Solid modelling,Pose,Solid modeling,Artificial intelligence,Simultaneous localization and mapping,Mobile robot | Conference |
ISBN | Citations | PageRank |
978-1-4577-0744-5 | 1 | 0.39 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Flavia Grosan | 1 | 1 | 0.39 |
| Alexandru Tandrau | 2 | 1 | 0.39 |
| Andreas Nüchter | 3 | 1341 | 90.03 |