Name
Abstract | ||
|---|---|---|
Service robots share the living space of humans. Thus, they should have a similar concept of the environment without having everything labeled beforehand. The detection of closed doors is challenging because they appear with different materials, designs and can even include glass inlays. At the same time their detection is vital in any kind of navigation tasks in domestic environments. A typical 2D object recognition algorithm may not be able to handle the large optical variety of doors. Improvements of low-cost infrared 3D-sensors enable robots to perceive their environment as spatial structure. Therefore we propose a novel door detection algorithm that employs basic structural knowledge about doors and enables to extract parts of doors from point clouds based on constraint region growing. These parts get weighted with Gaussian probabilities and are combined to create an overall probability measure. To show the validity of our approach, a realistic dataset of different doors from different angles and distances was acquired. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CRV.2014.44 | CRV |
Keywords | Field | DocType |
feature extraction,probability measure,glass,robot,mobile robots,gaussian processes,image segmentation,depth,door,point clouds,vision,kinect,3d,doors | Computer vision,Computer science,Probability measure,Feature extraction,Gaussian,Artificial intelligence,Region growing,Point cloud,Robot,Doors,Cognitive neuroscience of visual object recognition | Conference |
Citations | PageRank | References |
3 | 0.42 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastian Meyer Zu Borgsen | 1 | 18 | 4.01 |
| Matthias Schopfer | 2 | 48 | 4.56 |
| Leon Ziegler | 3 | 14 | 2.38 |
| Sven Wachsmuth | 4 | 267 | 43.83 |