General Hand–Eye Calibration Based on Reprojection Error Minimization

In this paper, we propose two novel methods for robot-world-hand–eye calibration and provide a comparative analysis against six state-of-the-art methods. We examine the calibration problem from two alternative geometrical interpretations, called 'hand–eye' and 'robot-world-hand–eye', respectively. The study analyses the effects of specifying the objective function as pose error or reprojection error minimization problem. We provide three real and three simulated datasets with rendered images as part of the study. In addition, we propose a robotic arm error modeling approach to be used along with the simulated datasets for generating a realistic response. The tests on simulated data are performed in both ideal cases and with pseudo-realistic robotic arm pose and visual noise. Our methods show significant improvement and robustness on many metrics in various scenarios compared to state-of-the-art methods.

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Methods for Simultaneous Robot-World Hand-Eye Calibration: A Comparative Study

10.20944/preprints201906.0182.v1 ◽

2019 ◽

Author(s):

Ihtisham Ali ◽

Olli Suominen ◽

Atanas Gotchev ◽

Emilio Ruiz Morales

Keyword(s):

Comparative Analysis ◽

Minimization Problem ◽

State Of The Art ◽

Simulated Data ◽

Error Modeling ◽

Robotic Arm ◽

Error Minimization ◽

Calibration Problem ◽

Reprojection Error ◽

Art Methods

In this paper, we propose two novel methods for robot-world/hand-eye calibration and provide a comparative analysis against six state-of-the-art methods. We examine the calibration problem from two alternative geometrical interpretations, called hand-eye and robot-world-hand-eye, respectively. The study analyses the effects of specifying the objective function as pose error or reprojection error minimization problem. We provide three real and three simulated datasets with rendered images as part of the study. In addition, we propose a robotic arm error modeling approach to be used along with the simulated datasets for generating a realistic response. The tests on simulated data are performed in both ideal cases and with pseudo-realistic robotic arm pose and visual noise. Our methods show significant improvement and robustness on many metrics in various scenarios compared to state-of-the-art methods.

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Contradiction between Conditions of Mean-Root Square Error Minimization and Integral Root Mean Estimate in Tracking Systems with the Principle of Deviation Control

Journal of Automation and Information Sciences ◽

10.1615/jautomatinfscien.v36.i1.50 ◽

2004 ◽

Vol 36 (1) ◽

pp. 38-49

Author(s):

Grigoriy F Zaitsev ◽

Grigoriy D. Radzivilov ◽

Nelya V. Gradoboyeva

Keyword(s):

Error Minimization ◽

Tracking Systems

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Error minimization based on multi-objective finite control set model predictive control for matrix converter in DFIG

International Journal of Electrical Power & Energy Systems ◽

10.1016/j.ijepes.2020.106575 ◽

2021 ◽

Vol 126 ◽

pp. 106575

Author(s):

Zafer Ortatepe ◽

Ahmet Karaarslan

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Matrix Converter ◽

Error Minimization ◽

Multi Objective ◽

Finite Control ◽

Control Set

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Orientation-Constrained System for Lamp Detection in Buildings Based on Computer Vision

Sensors ◽

10.3390/s19071516 ◽

2019 ◽

Vol 19 (7) ◽

pp. 1516 ◽

Cited By ~ 1

Author(s):

Francisco Troncoso-Pastoriza ◽

Pablo Eguía-Oller ◽

Rebeca Díaz-Redondo ◽

Enrique Granada-Álvarez ◽

Aitor Erkoreka

Keyword(s):

Computer Vision ◽

Optimization Methods ◽

Geometric Information ◽

Constrained System ◽

Building Information Modelling ◽

Correct Model ◽

Reprojection Error ◽

Localization Errors ◽

Building Information ◽

Error Filtering

Computer vision is used in this work to detect lighting elements in buildings with the goal of improving the accuracy of previous methods to provide a precise inventory of the location and state of lamps. Using the framework developed in our previous works, we introduce two new modifications to enhance the system: first, a constraint on the orientation of the detected poses in the optimization methods for both the initial and the refined estimates based on the geometric information of the building information modelling (BIM) model; second, an additional reprojection error filtering step to discard the erroneous poses introduced with the orientation restrictions, keeping the identification and localization errors low while greatly increasing the number of detections. These enhancements are tested in five different case studies with more than 30,000 images, with results showing improvements in the number of detections, the percentage of correct model and state identifications, and the distance between detections and reference positions.

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