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Paper WeA214.2

Alenyŗ, Guillem (CSIC-UPC), Dellen, Babette (Max Planck Institute for Dynamics and Self-Organization), Torras, Carme (CSIC - UPC)

3D Modelling of Leaves from Color and ToF Data for Robotized Plant Measuring

Scheduled for presentation during the Regular Sessions "Computer Vision for Robotics and Automation II" (WeA214), Wednesday, May 11, 2011, 10:20−10:35, Room 5J

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on December 10, 2019

Keywords Robotics in Agriculture and Forestry, Sensor Fusion, Computer Vision for Robotics and Automation

Abstract

Supervision of long-lasting extensive botanic experiments is a promising robotic application that some recent technological advances have made feasible. Plant modelling for this application has strong demands, particularly in what concerns 3D information gathering and speed. This paper shows that Time-of-Flight (ToF) cameras achieve a good compromise between both demands, providing a suitable complement to color vision. A new method is proposed to segment plant images into their composite surface patches by combining hierarchical color segmentation with quadratic surface fitting using ToF depth data. Experimentation shows that the interpolated depth maps derived from the obtained surfaces fit well the original scenes. Moreover, candidate leaves to be approached by a measuring instrument are ranked, and then robot-mounted cameras move closer to them to validate their suitability to being sampled. Some ambiguities arising from leaves overlap or occlusions are cleared up in this way. The work is a proof-of-concept that dense color data combined with sparse depth as provided by a ToF camera yields a good enough 3D approximation for automated plant measuring at the high throughput imposed by the application.

 

 

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