Name
Abstract | ||
|---|---|---|
As mobile robotics is gradually moving towards a level of semantic environment understanding, robust 3D object recognition plays an increasingly important role. One of the most crucial prerequisites for object recognition is a set of fast algorithms for geometry segmentation and extraction, which in turn rely on surface normal vectors as a fundamental feature. Although there exists a plethora of different approaches for estimating normal vectors from 3D point clouds, it is largely unclear which methods are preferable for online processing on a mobile robot. This paper presents a detailed analysis and comparison of existing methods for surface normal estimation with a special emphasis on the trade-off between quality and speed. The study sheds light on the computational complexity as well as the qualitative differences between methods and provides guidelines on choosing the 'right' algorithm for the robotics practitioner. The robustness of the methods with respect to noise and neighborhood size is analyzed. All algorithms are benchmarked with simulated as well as real 3D laser data obtained from a mobile robot. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ROBOT.2009.5152493 | ICRA |
Keywords | Field | DocType |
detailed analysis,normal vector,object recognition,surface normal estimation method,surface normal vector,mobile robot,mobile robotics,computational complexity,surface normal estimation,crucial prerequisite,robotics practitioner,feature extraction,mobile robots,computational geometry,robust control,computational modeling,image segmentation,estimation theory,vectors | Computer vision,Computer science,Computational geometry,Robustness (computer science),Feature extraction,Image segmentation,Artificial intelligence,Point cloud,Robotics,Mobile robot,Cognitive neuroscience of visual object recognition | Conference |
Volume | Issue | ISSN |
2009 | 1 | 1050-4729 |
Citations | PageRank | References |
72 | 2.67 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Klaas Klasing | 1 | 156 | 8.35 |
| Daniel Althoff | 2 | 137 | 6.47 |
| Dirk Wollherr | 3 | 673 | 60.01 |
| Martin Buss | 4 | 1799 | 159.02 |