Influence of Stereoscopic Camera System Alignment Error on the Accuracy of 3D Reconstruction

The article deals with the influence of inaccurate rotation of cameras in camera system alignment on 3D reconstruction accuracy. The accuracy of the all three spatial coordinates is analyzed for two alignments (setups) of 3D cameras. In the first setup, a 3D system with parallel optical axes of the cameras is analyzed. In this stereoscopic setup, the deterministic relations are derived by the trigonometry and basic stereoscopic formulas. The second alignment is a generalized setup with cameras in arbitrary positions. The analysis of the situation in the general setup is closely related with the influence of errors of the points' correspondences. Therefore the relation between errors of points' correspondences and reconstruction of the spatial position of the point was investigated. This issue is very complex. The worst case analysis was executed with the use of Monte Carlo method. The aim is to estimate a critical situation and the possible extent of these errors. Analysis of the generalized system and derived relations for normal system represent a significant improvement of the spatial coordinates accuracy analysis. A practical experiment was executed which confirmed the proposed relations

Método local de correção da distorção da lente aplicado a visão estereoscópica

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2014.Visão estereoscópica é o processo de estimação da informação de profundidade de uma cena, ou de um objeto em particular, por meio da análise de duas imagens capturadas em diferentes pontos de vista, usando um modelo apropriado de câmera. As câmeras permitem uma rica representação da cena quando comparadas a outros sistemas, como laser, radar e sonar, sendo cada vez mais usadas em aplicações de robótica móvel e do setor automotivo, tais como navegação autônoma e detecção de objetos e obstáculos. Sistemas de visão estereoscópica são também utilizados em aplicações de metrologia e de sensoriamento remoto, e são caracterizados por três etapas principais: calibração, registro de imagens e reconstrução. A reconstrução da informação de profundidade em sistemas de visão estereoscópica é influenciada pela distorção das lentes. Neste trabalho, estuda-se o comportamento do erro de reconstrução em função do aumento da ordem do modelo de correção da distorção e propõe-se um novo método de correção da distorção de lentes, baseado na estimação de um conjunto de coeficientes de correção da distorção para cada região da imagem. A avaliação do sistema, feita por simulação com imagens sintéticas, indica que a aplicação do método proposto possibilita obter erro de reconstrução menor que o obtido pela aplicação do método convencional.Abstract : Stereo Vision is the process of recovery of three-dimensional information of a scene, or an object of scene, from the analysis of two bi-dimensional images by using an appropriate camera model. The cameras allow for a rich representation of the scene when compared to others types of sensors, such as laser and sonar, being used more and more in applications for mobile robotics and assistance driving, such as object and obstacle detection and localization. Stereo Vision is also used in remote sensing and Metrology, and is composed of three main steps: camera calibration, pixel correspondence and 3-D reconstruction. Lens distortion is one of the main factors that limits the accuracy of stereo vision system reconstruction. We propose a new method for correction of the lens distortion by applying compensation to each region of an image. Our method splits the image into smaller regions and compensates for each region for a fixed lenses model order. When compared to the conventional method, which models the entire image with only one model, our approach provides better compensation and reduce the depth error as show in the experiments with synthetic data

A contribution to situation analysis in predictive pedestrian protection

The subject of this thesis is the development and prototypical realization of a driver assistance system with the purpose of predictive pedestrian protection. The system shall help the driver to avoid collisions with pedestrians by issuing a noticeable warning to the driver prior to a possible collision. If the driver fails to react to the warning and the criticality of the situation increases, the system will initiate an automatic braking intervention in order to prevent or mitigate the collision. One of the main challenges for the system is to correctly estimate the risk of an impending collision. For a successful driver warning it is necessary to issue the warning early enough, enabling the driver to react. Therefore, the time before the collision, at which the decision to warn the driver is made, has to be significantly longer than the reaction time of the driver (up to 2 s). Because of this comparatively long time span it is vital for the system to predict the possible movements of the pedestrian as accurately as possible. This also holds true for the automatic braking intervention. With a high assumption placed upon the movement capabilities of pedestrians, the decision for an automatic braking is not possible up to a few hundred milliseconds before the collision, rendering the benefit of the system comparatively small. Therefore, the first part of the thesis concentrates on the development of a situation analysis approach which considers the movement capabilities of pedestrians. The possible and relevant trajectories of pedestrians in typical accident scenarios are analyzed and contribute to the development of the pedestrian motion model. For this model, a test study is designed and conducted to measure the movement capabilities of different test persons in relevant situations. The results of the study are analyzed and integrated into the model. The prediction of the movement capabilities depends on the current velocity of the pedestrian, as well as the available time and direction of movement, which yields significant improvements of the results in the situation analysis. The results show that a decision for an automatic braking intervention based upon the prediction of an unavoidable collision can be made earlier which leads to a reduction in the collision velocity. The second part of the thesis analyzes the sensor system which is used to recognize pedestrians in front of the vehicle and the impact of this system's errors in the situation analysis. The prototypical realization of the system uses a stereo-vision system in order to detect pedestrians and to measure relevant data, for instance position and velocity of the pedestrian relative to the vehicle. The quality of this data is vital for the system to function, therefore, the implications of erroneous data are analyzed, and the requirements for the relevant input data are derived. For this purpose, a sensitivity analysis with a series of simulations is conducted. The artificial sensor data in the simulations is superimposed by artificial noise in order to determine the acceptable degree of noise for the system. The type of noise depends on the data and is derived from the analysis (theoretical as well as practical) of the stereo-vision system. The thesis is concluded by presenting the test-vehicle and an analysis of the system performance in 50 hours of test driving in urban areas

Error Evaluation in a Stereovision-Based 3D Reconstruction System

The work presented in this paper deals with the performance analysis of the whole 3D reconstruction process of imaged objects, specifically of the set of geometric primitives describing their outline and extracted from a pair of images knowing their associated camera models. The proposed analysis focuses on error estimation for the edge detection process, the starting step for the whole reconstruction procedure. The fitting parameters describing the geometric features composing the workpiece to be evaluated are used as quality measures to determine error bounds and finally to estimate the edge detection errors. These error estimates are then propagated up to the final 3D reconstruction step. The suggested error analysis procedure for stereovision-based reconstruction tasks further allows evaluating the quality of the 3D reconstruction. The resulting final error estimates enable lastly to state if the reconstruction results fulfill a priori defined criteria, for example, fulfill dimensional constraints including tolerance information, for vision-based quality control applications for example.</p

Error Evaluation in a Stereovision-Based 3D Reconstruction System

The work presented in this paper deals with the performance analysis of the whole 3D reconstruction process of imaged objects, specifically of the set of geometric primitives describing their outline and extracted from a pair of images knowing their associated camera models. The proposed analysis focuses on error estimation for the edge detection process, the starting step for the whole reconstruction procedure. The fitting parameters describing the geometric features composing the workpiece to be evaluated are used as quality measures to determine error bounds and finally to estimate the edge detection errors. These error estimates are then propagated up to the final 3D reconstruction step. The suggested error analysis procedure for stereovision-based reconstruction tasks further allows evaluating the quality of the 3D reconstruction. The resulting final error estimates enable lastly to state if the reconstruction results fulfill a priori defined criteria, for example, fulfill dimensional constraints including tolerance information, for vision-based quality control applications for example