A sensor-to-pattern calibration framework for multi-modal industrial collaborative cells

TL;DR

This paper introduces a unified calibration framework for multi-modal sensors in collaborative robotic cells, enabling accurate and efficient sensor fusion for safety and perception.

Contribution

It presents a novel sensor-to-pattern calibration method that handles multiple sensor modalities in a single optimization process.

Findings

Successfully calibrates RGB, Depth, and LiDAR sensors in complex collaborative cells.

Achieves high calibration accuracy across multiple sensor types.

Streamlines the calibration process for multi-modal robotic environments.

Abstract

Collaborative robotic industrial cells are workspaces where robots collaborate with human operators. In this context, safety is paramount, and for that a complete perception of the space where the collaborative robot is inserted is necessary. To ensure this, collaborative cells are equipped with a large set of sensors of multiple modalities, covering the entire work volume. However, the fusion of information from all these sensors requires an accurate extrinsic calibration. The calibration of such complex systems is challenging, due to the number of sensors and modalities, and also due to the small overlapping fields of view between the sensors, which are positioned to capture different viewpoints of the cell. This paper proposes a sensor to pattern methodology that can calibrate a complex system such as a collaborative cell in a single optimization procedure. Our methodology can tackle RGB and Depth cameras, as well as LiDARs. Results show that our methodology is able to accurately calibrate a collaborative cell containing three RGB cameras, a depth camera and three 3D LiDARs.