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

Rato, Daniela; Oliveira, Miguel; Santos, Vítor; Gomes, Manuel; Sappa, Ángel D.

doi:10.1016/j.jmsy.2022.07.006

Cited by 8 publications

(7 citation statements)

References 27 publications

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“…This system is a hand–eye calibration system, which is simultaneously a sensor in motion and sensor to sensor calibration problem. LARCC [ 17 ] ( Figure 16 ) is a collaborative cell equipped with a robotic manipulator, three RGB cameras, three 3D LiDARs and one depth camera. This system is a sensor-to-sensor calibration problem, with a large number of sensors and modalities, which makes the problem of calibrating such a system very challenging.…”

Section: Resultsmentioning

confidence: 99%

“…ATOM is a methodology that was proven to accurately calibrate several robotic systems. Additional details on the methodology itself are provided in [ 1 ], and usage cases of varied robotic systems can be read in [ 14 , 15 , 16 , 17 ]. This paper is focused not on the calibration methodology of ATOM, but rather on the support functionalities that create an integrated solution for the calibration of robotic systems.…”

Section: Related Workmentioning

confidence: 99%

“…The depth sensor modality yields a representation for the labels similar to the one previously described for the LiDAR modality [ 17 ]. It contains pattern points (magenta points in Figure 9 ), and also boundary points (yellow points in Figure 9 ).…”

Section: Proposed Frameworkmentioning

confidence: 99%

“… LARCC is a collaborative cell with a robotic manipulator, three RGB cameras, three 3D LiDARs and one depth camera [ 17 ]. ( a ) represents the real system and ( b ) represents the simulated system.…”

Section: Figurementioning

confidence: 99%

“…Our previous work focused on the development of the Atomic Transformations Optimization Method’s (ATOM) calibration methodology. In particular, we worked on expanding the methodology for tackling hand–eye calibration problems [ 14 ], agricultural robots [ 15 ], autonomous vehicles [ 16 ] and collaborative cells [ 17 ]. In addition, we have shown several examples of successful calibrations [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

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ATOM Calibration Framework: Interaction and Visualization Functionalities

Gomes

Oliveira

Santos

2023

Sensors

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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.

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Section: Resultsmentioning

confidence: 99%