Quantification of uncertainty in a stereoscopic particle image velocimetry measurement

In Stereoscopic Particle Image Velocimetry (Stereo-PIV), the three velocity components are obtained by illuminating a planar region in the flow field and recording the region of interest using two cameras at an angle. Calibration, planar velocity estimation, and velocity reconstruction are the three essential steps involved in the process. Earlier efforts to quantify the accuracy in a Stereo-PIV measurement process have shown higher error in out of plane motion. However, a detailed analysis of the measurement uncertainty involved in a Stereo-PIV calibration-based reconstruction process has yet to be presented. This analysis provides a detailed framework to specify the uncertainty in the coefficients of the calibration mapping function and the uncertainty involved in self-calibration step for correction of the registration error. Using Taylor series expansion for uncertainty propagation the contribution of the calibration step uncertainties are combined with planar field uncertainties to predict the overall uncertainty in the reconstructed velocity components. The analysis is tested using simulated random field images and experimental vortex ring images. The results emphasize the sensitivity and interdependence of the individual uncertainties involved in each step of a Stereo-PIV measurement process

TEScalib: Targetless Extrinsic Self-Calibration of LiDAR and Stereo Camera for Automated Driving Vehicles with Uncertainty Analysis

In this paper, we present TEScalib, a novel extrinsic self-calibration approach of LiDAR and stereo camera using the geometric and photometric information of surrounding environments without any calibration targets for automated driving vehicles. Since LiDAR and stereo camera are widely used for sensor data fusion on automated driving vehicles, their extrinsic calibration is highly important. However, most of the LiDAR and stereo camera calibration approaches are mainly target-based and therefore time consuming. Even the newly developed targetless approaches in last years are either inaccurate or unsuitable for driving platforms. To address those problems, we introduce TEScalib. By applying a 3D mesh reconstruction-based point cloud registration, the geometric information is used to estimate the LiDAR to stereo camera extrinsic parameters accurately and robustly. To calibrate the stereo camera, a photometric error function is builded and the LiDAR depth is involved to transform key points from one camera to another. During driving, these two parts are processed iteratively. Besides that, we also propose an uncertainty analysis for reflecting the reliability of the estimated extrinsic parameters. Our TEScalib approach evaluated on the KITTI dataset achieves very promising results

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

Calibration and Sensitivity Analysis of a Stereo Vision-Based Driver Assistance System

Az http://intechweb.org/ alatti "Books" fül alatt kell rákeresni a "Stereo Vision" címre és az 1. fejezetre

Structured Light-Based 3D Reconstruction System for Plants.

Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance

3D Reconstruction with Low Resolution, Small Baseline and High Radial Distortion Stereo Images

In this paper we analyze and compare approaches for 3D reconstruction from low-resolution (250x250), high radial distortion stereo images, which are acquired with small baseline (approximately 1mm). These images are acquired with the system NanEye Stereo manufactured by CMOSIS/AWAIBA. These stereo cameras have also small apertures, which means that high levels of illumination are required. The goal was to develop an approach yielding accurate reconstructions, with a low computational cost, i.e., avoiding non-linear numerical optimization algorithms. In particular we focused on the analysis and comparison of radial distortion models. To perform the analysis and comparison, we defined a baseline method based on available software and methods, such as the Bouguet toolbox [2] or the Computer Vision Toolbox from Matlab. The approaches tested were based on the use of the polynomial model of radial distortion, and on the application of the division model. The issue of the center of distortion was also addressed within the framework of the application of the division model. We concluded that the division model with a single radial distortion parameter has limitations