publications

2023

1. ATOM Calibration Framework: Interaction and Visualization Functionalities

   Manuel Gomes, Miguel Oliveira, and Vítor Santos

   Sensors, Jan 2023

   Abs

   Robotic systems are evolving to include a large number of sensors and diverse sensor modalities. In order to operate a system with multiple sensors, the geometric transformations between those sensors must be accurately estimated. The process by which these transformations are estimated is known as sensor calibration. Behind every sensor calibration approach is a formulation and a framework. The formulation is the method by which the transformations are estimated. The framework is the set of operations required to carry out the calibration procedure. This paper proposes a novel calibration framework that gives more flexibility, control and information to the user, enhancing the user interface and the user experience of calibrating a robotic system. The framework consists of several visualization and interaction functionalities useful for a calibration procedure, such as the estimation of the initial pose of the sensors, the data collection and labeling, the data review and correction and the visualization of the estimation of the extrinsic and intrinsic parameters. This framework is supported by the Atomic Transformations Optimization Method formulation, referred to as ATOM. Results show that this framework is applicable to various robotic systems with different configurations, number of sensors and sensor modalities. In addition to this, a survey comparing the frameworks of different calibration approaches shows that ATOM provides a very good user experience.

2022

1. A Sensor-to-Pattern Calibration Framework for Multi-Modal Industrial Collaborative Cells

   Daniela Rato, Miguel Oliveira, Vítor Santos, and 2 more authors

   Journal of Manufacturing Systems, Jul 2022

   Abs

   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.