Virtual Training for a Real Application: Accurate Object-Robot Relative Localization without Calibration
This enables autonomous robotic manipulation in uncontrolled environments like construction sites, offering a practical solution by eliminating the need for expensive real data acquisition.
The paper tackles the problem of accurately localizing objects relative to robots using only 3D models and uncalibrated cameras, achieving millimetric errors without real training data by training a CNN on synthetic scenes.
Localizing an object accurately with respect to a robot is a key step for autonomous robotic manipulation. In this work, we propose to tackle this task knowing only 3D models of the robot and object in the particular case where the scene is viewed from uncalibrated cameras -- a situation which would be typical in an uncontrolled environment, e.g., on a construction site. We demonstrate that this localization can be performed very accurately, with millimetric errors, without using a single real image for training, a strong advantage since acquiring representative training data is a long and expensive process. Our approach relies on a classification Convolutional Neural Network (CNN) trained using hundreds of thousands of synthetically rendered scenes with randomized parameters. To evaluate our approach quantitatively and make it comparable to alternative approaches, we build a new rich dataset of real robot images with accurately localized blocks.