Name
Abstract | ||
|---|---|---|
Tactile sensing can be an important source of information for robots, and texture discrimination in particular is useful in object recognition and terrain identification. Whisker based tactile sensing has recently been shown to be a promising approach for mobile robots, using simple sensors and many classification approaches. However these approaches have often been tested in limited environments, and have not been compared against one another in a controlled way. A wide range of whisker-object contact poses are possible on a mobile robot, and the effect such contact variability has on sensing has not been properly investigated. We present a novel, carefully controlled study of simple surface texture classifiers on a large set of varied pose conditions that mimic those encountered by mobile robots. Namely, single brief whisker contacts with textured surfaces at a range of surface orientations and contact speeds. Results show that different classifiers are appropriate for different settings, with spectral template and feature based approaches performing best in surface texture, and contact speed estimation, respectively. The results may be used to inform selection of classifiers in tasks such as tactile SLAM. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IROS.2012.6385634 | Intelligent Robots and Systems |
Keywords | Field | DocType |
SLAM (robots),estimation theory,geometry,image classification,image texture,mobile robots,object recognition,pose estimation,surface texture,tactile sensors,brief whisker contacts,contact speed estimation,contact speeds,contact variability,information source,mobile robots,object recognition,pose conditions,spectral template,surface orientations,surface texture classifiers,tactile SLAM,terrain identification,texture discrimination,textured surfaces,uncertain contact pose geometry,whisker based tactile sensing,whisker-object contact poses,whiskered texture classification | Computer vision,Image texture,Computer science,Terrain,Pose,Artificial intelligence,Robot,Contextual image classification,Mobile robot,Tactile sensor,Cognitive neuroscience of visual object recognition | Conference |
ISSN | ISBN | Citations |
2153-0858 | 978-1-4673-1737-5 | 2 |
PageRank | References | Authors |
0.40 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mathew H. Evans | 1 | 72 | 10.13 |
| Martin J. Pearson | 2 | 215 | 26.34 |
| Nathan F. Lepora | 3 | 239 | 35.36 |
| Tony J. Prescott | 4 | 145 | 15.77 |
| Charles W. Fox | 5 | 162 | 14.65 |