Sweet pepper pose detection and grasping for automated crop harvesting
Christopher F. Lehnert, Inkyu Sa, Christopher McCool, Ben Upcroft, Tristan Perez
Abstract
This paper presents a method for estimating the 6DOF pose of sweet-pepper (capsicum) crops for autonomous harvesting via a robotic manipulator. The method uses the Kinect Fusion algorithm to robustly fuse RGB-D data from an eye-in-hand camera combined with a colour segmentation and clustering step to extract an accurate representation of the crop. The 6DOF pose of the sweet peppers is then estimated via a nonlinear least squares optimisation by fitting a superellipsoid to the segmented sweet pepper. The performance of the method is demonstrated on a real 6DOF manipulator with a custom gripper. The method is shown to estimate the 6DOF pose successfully enabling the manipulator to grasp sweet peppers for a range of different orientations. The results obtained improve largely on the performance of grasping when compared to a naive approach, which does not estimate the orientation of the crop.
BibTeX
@inproceedings{icra2016_sweetpepperposed,
title = {Sweet pepper pose detection and grasping for automated crop harvesting},
author = {Christopher F. Lehnert and Inkyu Sa and Christopher McCool and Ben Upcroft and Tristan Perez},
booktitle = {ICRA 2016},
year = {2016}
}