Robust plane-based calibration for linear cameras

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Calibração de sistema de visão com câmera de linha

O trabalho prevê o desenvolvimento de um algoritmo de calibração de um sistema de visão composto por uma câmera de linha sobre uma esteira, incluindo parâmetros não lineares de distorção da lente. A captura de imagens bidimensionais ocorre, nesse sistema, pelo movimento de objeto sobre a esteira, passando pelo campo de visão da câmera. Cada fotografia registra uma linha que, unidas de forma sequencial, formam uma representação 2D do objeto. O procedimento de calibração explora a identificação do modelo do sis tema por Aprendizado de Máquina, permitindo descrever matematicamente a projeção de objetos 3D em uma imagem. A proposição é: parametrizar e montar o sistema de visão; projetar e confeccionar o padrão de calibração; implementar algoritmo de calibração com tecnologia de Aprendizado Profundo; realizar ensaios de calibração; avaliar os resulta dos em termos de convergência e erro de reprojeção; e revisão do método, discutindo seu impacto e enunciando possibilidades de desenvolvimento futuroThe work envisages the development of a calibration algorithm for a vision system comprised of a line scan camera placed on a conveyor belt, encompassing non-linear lens distortion parameters. In this system, two-dimensional image capture occurs as objects move across the conveyor belt, traversing the camera’s field of view. Each photograph records a line which, when sequentially joined, forms a 2D representation of the object. The calibration procedure leverages Machine Learning to identify the system’s model, enabling the mathematical description of the projection of 3D objects onto an image. The proposition is to parameterize and assemble the vision system; design and create the ca libration pattern; implement the calibration algorithm using Deep Learning technology; conduct calibration tests; evaluate the results in terms of convergence and reprojection error; and review the method, discussing its impact and outlining possibilities for future developmen

External multi-modal imaging sensor calibration for sensor fusion: A review

Multi-modal data fusion has gained popularity due to its diverse applications, leading to an increased demand for external sensor calibration. Despite several proven calibration solutions, they fail to fully satisfy all the evaluation criteria, including accuracy, automation, and robustness. Thus, this review aims to contribute to this growing field by examining recent research on multi-modal imaging sensor calibration and proposing future research directions. The literature review comprehensively explains the various characteristics and conditions of different multi-modal external calibration methods, including traditional motion-based calibration and feature-based calibration. Target-based calibration and targetless calibration are two types of feature-based calibration, which are discussed in detail. Furthermore, the paper highlights systematic calibration as an emerging research direction. Finally, this review concludes crucial factors for evaluating calibration methods and provides a comprehensive discussion on their applications, with the aim of providing valuable insights to guide future research directions. Future research should focus primarily on the capability of online targetless calibration and systematic multi-modal sensor calibration.Ministerio de Ciencia, Innovación y Universidades | Ref. PID2019-108816RB-I0