Robust Task-Oriented Markerless Extrinsic Calibration for Robotic Pick-and-Place Scenarios

Abstract

Camera extrinsic calibration is an important module for robotic visual tasks. A typical visual task is to use a robot and a color camera to pick an object from a variety of items and place it in a designated area. However, the noise of multi-sensor processing may have a significant impact on the results when running a full-process visual task; in addition, checkerboards are inconvenient or unavailable in pick-andplace scenarios. In this paper, we propose and develop a task-oriented markerless hand-eye calibration method by using nonlinear iterative optimization. The optimization employs a transfer error to construct cost function, which is necessarily observable and estimable for visual tasks. Our method does not require a calibration checkerboard and only uses an available saliency object in the task scene as a marker. It provides an end-to-end method that converts extrinsic parameters into variables that are optimized with the cost function, making it not only robust to sensors with noise but also able to meet the requirements of the tasks' reconstruction accuracy. Different from classic methods detecting a known size calibration pattern, the input of our method is a batch of image points and the corresponding world points. The results show that the accuracy of our extrinsic calibration method is sufficient for the robot's pick-and-place tasks. The experiments of the competition demonstrate that our method is definitely effective in the desired tasks of vision-in-the-loop automatic pick-and-place scenarios.

Robotic Pick-and-Place Time Optimization: Application to Footwear Production

“…In pick-and-place operation, the calibration is an important parameter. In [39] a task-oriented marker less handeye calibration method is developed by using non-linear iterative optimization in order to improve the calibration task The image processing system is based on an external ABB camera connected with a PC, where a RobotStudio program is developed. To cover as many possibilities as possible, 250 samples are collected in random positions, Figure 1c, due to in the industrial process, the pieces could get in any position.…”

A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots

“…Emerging applications demand that industrial robots not only be faster, but also be able to accurately identify and find parts that are randomly placed on moving conveyors, stacked in containers, or on pallets [14]- [16]. Machine vision systems, which have been around for decades, are now being used in conjunction with robotics to aid automation systems in the processing of such components [17], [18].…”