Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-25T03:16:26.640Z Has data issue: false hasContentIssue false
  • English
  • Français

3D Shape and surface colour sensor fusion for robot vision

Published online by Cambridge University Press:  09 March 2009

R. A. Jarvis
R. A. Jarvis
Affiliation:
Intelligent Robotics Research Centre, Monash University, Clayton, Victoria (Australia) 3168
Get access Rights & Permissions [Opens in a new window]

Summary

This paper argues the case for extracting as complete a set of sensory data as practicable from scenes consisting of complex assemblages of objects with the goal of completing the task of scene analysis, including placement, pose, identity and relationship amongst the components in a robust manner which supports goal directed robotic action, including collision-free trajectory planning, grip site location and manipulation of selected object classes.

The emphasis of the paper is that of sensor fusion of range and surface colour data including preliminary results in proximity, surface normal directionality and colour based scene segmentation through semantic-free clustering processes. The larger context is that of imbedding the results of such analysis in a graphics world containing an articulated robotic manipulator and of carrying out experiments in that world prior to replication of safe manipulation sequences in the real world.

Keywords

Robot visionSensor fusionScene analysisTasks.
Type
Article
Information
Robotica , Volume 10 , Issue 5 , September 1992 , pp. 389 - 396
Copyright
Copyright © Cambridge University Press 1992

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Jarvis, R.A., “A Semantic-Free Approach to 3D Robot Colour Vision”, In: Three-Dimensional Machine Vision, edited by Prof. Kanade, Takeo (Kluwer Academic Publishers, U.S.A. 1987) pp. 565–609.CrossRefGoogle Scholar
2.Jarvis, R.A., “A Perspective on Range Finding Techniques for Computer Vision” IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-5, No. 2, 122–139 (03, 1983).CrossRefGoogle Scholar
3.Besl, P.J. and Jain, R.C., “Three Dimensional Object Recognition” Computing Surveys 17(1), 75–145 (03 1985).CrossRefGoogle Scholar
4.Cheung, C.C. and Brown, W.A., “3D Shape Measurement using Three Camera Stereopsis” SPIE Proceedings 850, 128–135 (11 1987).CrossRefGoogle Scholar
5.Jarvis, R.A., “Quad-Vision Ranging for Robotic Application” 4th Australian Joint Conference on Artificial Intelligence, 11 21–23, 1990, Hyatt Regency, Perth, Western Australia (1990) pp. 682–698.Google Scholar
6.Pentland, A.P., “A New Sense for Depth of Field” IEEE Trans. Pattern Anal, and Machine Intelligence PAMI-9, No. 4523–531 (07 1987).CrossRefGoogle Scholar
7.Jarvis, R.A., “Range from Out-of-Focus Blur” Proc. A.I., '88 – Australian Joint Artificial Intelligence Conference,Adelaide,15–18 Noumber 1988 (1988) pp. 356–372.Google Scholar
8.Jarvis, R.A., “Range from Brightness for Robotic Vision” Proc. 4th International Conf. on Robot Vision and Sensory Controls, London, Oct. 1984 (1984) pp. 165–172.Google Scholar
9.Horn, B.K.P. and Brooks, M.J. (Editors), Shape from Shading (The MIT Press, U.S.A., 1989).Google Scholar
10.Alexander, B.F. and Ng, K.C., “3D Shape Measurement by Active Triangulation using an Array of Coded Light Stripes” SPIE 850, 199–209 (11 1987).Google Scholar
11.Alexander, B.F., “High Accuracy Non Contact Three Shape Dimensional Measurement” PhD thesis (Department of Electrical and Computer Systems Engineering, Monash University, 09, 1989).Google Scholar
12.Nitzan, D., Brian, A.E. and Duda, R.O., “The Measurement and Use of Registered and Range Data in Scene Analysis” Proc. IEEE 65, 206–220 (02 1977).CrossRefGoogle Scholar
13.Jarvis, R.A., “A Laser time-of-Flight Range Scanner for Robotic Vision” IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-5, No. 5, 505–512 (09 1983).CrossRefGoogle Scholar
14.Jarvis, R.A. and Patrick, E.A., “Clustering Using a Similarity Measure Based on Shared Near Neighbours” IEEE Transactions on Electronic Computers C-22, No. 11, 1025–1034 (11 1973).CrossRefGoogle Scholar
15.Jarvis, R.A., “Region Based Image Segmentation Using Shared Near Neighbour Clustering” Proceedings International Conference on Cybernetics and Society,Washington, D.C.,19–21 September, 1977 (1977) pp. 641–647.Google Scholar
16.Jarvis, R.A., “Structured Merge Strategies for Image Segmentation” Proceedings IEEE COMSAC 77 Conference,8–11 noumber, 1977 (1977) pp. 472–485.Google Scholar
17.Jarvis, R.A., “Expedient Range Enhanced 3D Robot Colour Vision” Robotica 1, part 1, 25–31 (1983).CrossRefGoogle Scholar
18.Sedgewick, R., Algorithms, 2nd Edition (Addison Wesley Publishing Co. U.S.A., 1988).Google Scholar
19.Besl, P.J. and Jain, R.C., “Invariant Surface Characteristics for 3D Object Recognition in Range Images” Computer Vision, Graphics and Image Processing 33(1) 33–80 (1986).CrossRefGoogle Scholar
20.Foong, K.Y. and Jarvis, R.A., “Robotic Construction with Sequence Planning” The Third National Conference on Robotics,June 3–6, 1990,Melbourne, Victoria (1990) pp. 165–176.Google Scholar
21.Jarvis, R.A., “Configuration Space Collision-Free Path Planning for Robotic Manipulators” Proc. 10th Australian Computer Science Conference,Deakin University,Victoria,4–6 February 1987 (1987) pp. 193–204.Google Scholar