Evaluation of an interactive technique for creating site models from range data

Recognizing and locating objects is crucial to robotic operations in unstructured environments. To satisfy this need, we have developed an interactive system for creating object models from range data based on simulated a annealing and supervisory control This interactive modeling system maximizes the advantages of both manual and autonomous methods while minimizing their weaknesses. Therefore, it should outperform purely autonomous or manual techniques. We have designed and executed experiments for the purpose of evaluating the performance of our application as compared to an identical but purely manually driven application. These experiments confirmed the following hypotheses: (1) Interactive modeling should outperform purely manual modeling in total task time and fitting accuracy. (2) Operator effort decreases significantly when utilizing interactive modeling. (3) User expertise does not have a significant effect on interactive modeling task time. (4) Minimal human interaction will increase performance on {open_quotes}easy{close_quotes} scenes. Using 14 subjects and 8 synthetically generated scenes, we recorded the task times and pose data and, from them, used analysis of variance (ANOVA) to test a set of hypotheses.

[1]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[2]  Ramesh C. Jain,et al.  Building an environment model using depth information , 1989, Computer.

[3]  Thomas B. Sheridan,et al.  Telerobotics, Automation, and Human Supervisory Control , 2003 .

[4]  S. Lee,et al.  Intelligent sensing and control for advanced teleoperation , 1992, IEEE Control Systems.

[5]  W. Eric L. Grimson,et al.  An automatic registration method for frameless stereotaxy, image guided surgery, and enhanced reality visualization , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[6]  M. Kendall.  Elementary Statistics , 1945, Nature.

[7]  William H. Press,et al.  Numerical Recipes in C, 2nd Edition , 1992 .

[8]  Paul G. Hoel,et al.  Elementary statistics , 1971 .

[9]  Karl Heinz Hoffmann,et al.  The optimal simulated annealing schedule for a simple model , 1990 .

[10]  Martin D. Levine,et al.  Recovering parametric geons from multiview range data , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Alok Gupta,et al.  The extruded generalized cylinder: a deformable model for object recovery , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Michael J. McDonald,et al.  Graphical programming: On-line robot simulation for telerobotic control , 1993 .

[14]  Andrew E. Johnson,et al.  Sensor-based interior modeling , 1995 .

[15]  Volker Graefe,et al.  Applications of dynamic monocular machine vision , 1988, Machine Vision and Applications.

[16]  Ruzena Bajcsy,et al.  Segmentation of range images as the search for geometric parametric models , 1995, International Journal of Computer Vision.

[17]  John R. Spofford,et al.  Machine-vision-based teleoperation aid , 1991 .

[18]  R. Bajcsy,et al.  Three dimensional object representation revisited , 1987 .

[19]  Thenkurussi Kesavadas,et al.  Virtual reality based point-and-direct robotic system with instrumented glove , 1994 .

[20]  Ramakant Nevatia,et al.  Including interaction in an automated modelling system , 1995, Proceedings of International Symposium on Computer Vision - ISCV.