Name
Abstract | ||
|---|---|---|
The components of the Sheffield Artificial Intelligence Vision Research Unit (AIVRU) three-dimensional (3D) vision sys tem, which currently supports model-based object recognition and location, are described. Its potential for robotics applica tions is demonstrated by its guidance of a Universal Machine Intelligence robot arm in a pick-and-place task. The system comprises (1) the recovery of a sparse depth map using edge- based, passive stereo triangulation; (2) the grouping, descrip tion, and segmentation of edge segments to recover a 3D representation of the scene geometry in terms of straight lines and circular arcs; (3) the statistical combination of 3D de scriptions for object model creation from multiple stereo views and the propagation of constraints for within-view re finement ; and (4) the matching of 3D wireframe object models to 3D scene descriptions in order to recover an initial estimate of their position and orientation. |
Year | DOI | Venue |
|---|---|---|
1989 | 10.1177/027836498900800401 | INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH |
Keywords | Field | DocType |
geometric modelling,robot arm,depth map,object recognition,object model,three dimensional,machine intelligence,geometric model,artificial intelligent | Computer vision,Robotic arm,Segmentation,Computer science,Image processing,Object model,Triangulation (social science),Artificial intelligence,Depth map,Robotics,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
8 | 4 | 0278-3649 |
Citations | PageRank | References |
17 | 7.19 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephen Pollard | 1 | 17 | 7.19 |
| Tony P. Pridmore | 2 | 143 | 40.24 |
| John Porrill | 3 | 352 | 85.11 |
| John E. W. Mayhew | 4 | 233 | 322.10 |
| John P. Frisby | 5 | 133 | 289.80 |