High-resolution, real-time simultaneous 3D surface geometry and temperature measurement

Nondestructive thermographic detection of internal defects using pixel-pattern based laser excitation and photothermal super resolution reconstruction

In this work, we present a novel approach to photothermal super resolution based thermographic resolution of internal defects using two-dimensional pixel pattern-based active photothermal laser heating in conjunction with subsequent numerical reconstruction to achieve a high-resolution reconstruction of internal defect structures. With the proposed adoption of pixelated patterns generated using laser coupled high-power DLP projector technology the complexity for achieving true two-dimensional super resolution can be dramatically reduced taking a crucial step forward towards widespread practical viability. Furthermore, based on the latest developments in high-power DLP projectors, we present their first application for structured pulsed thermographic inspection of macroscopic metal samples. In addition, a forward solution to the underlying inverse problem is proposed along with an appropriate heuristic to find the regularization parameters necessary for the numerical inversion in a laboratory setting. This allows the generation of synthetic measurement data, opening the door for the application of machine learning based methods for future improvements towards full automation of the method. Finally, the proposed method is experimentally validated and shown to outperform several established conventional thermographic testing techniques while conservatively improving the required measurement times by a factor of 8 compared to currently available photothermal super resolution techniques.

On-the-fly extrinsic calibration of multimodal sensing system for fast 3D thermographic scanning

The fusion of three-dimensional (3D) geometrical and two-dimensional (2D) thermal information provides a promising method for characterizing temperature distribution of 3D objects, extending infrared imaging from 2D to 3D to support various thermal inspection applications. In this paper, we present an effective on-the-fly calibration approach for accurate alignment of depth and thermal data to facilitate dynamic and fast-speed 3D thermal scanning tasks. For each pair of depth and thermal frames, we estimate their relative pose by minimizing the objective function that measures the temperature consistency between a 2D infrared image and the reference 3D thermographic model. Our proposed frame-to-model mapping scheme can be seamlessly integrated into a generic 3D thermographic reconstruction framework. Through graphics-processing-unit-based acceleration, our method requires less than 10 ms to generate a pair of well-aligned depth and thermal images without hardware synchronization and improves the robustness of the system against significant camera motion.

Relative pose estimation for multiple cameras using Lie algebra optimization

This paper addresses the problem of relative pose estimation for multiple cameras in the context of motion-based camera calibration. Relative pose is found from a set of camera-target relative poses. A least-square kind loss function is established using 3D relative Riemannian metrics of targets and cameras. Lie algebra-based solvers are proposed for orientation and pose estimation. Multiple cameras are calibrated without common targets or features in space using our proposed framework. The simulation and real data evaluation demonstrate that our proposed algorithms can achieve high accuracy with limited and precise camera-target poses.

Detection of material zones on the surface of a steel-aluminum hybrid component using reflection models and a monochromatic fringe projection profilometry system

The limits of traditional lightweight engineering are being extended by the development of hybrid components. Lightweight potential is especially high when using dissimilar materials, e.g., a friction-welded steel-aluminum combination. An important factor for the mechanical properties of such a combination is the geometry and location of the joining zone between the materials. The geometry of these objects can be reconstructed by optical triangulation techniques such as fringe projection profilometry. In this paper, we present a method to robustly detect the different material zones on the surface of a hybrid steel-aluminum component. We use reflection models and data from a fringe projection profilometry system. The intensity values and 3D geometry data from the fringe projection system are used to estimate material-specific reflection parameters for each 3D point and detect different material areas based on a global threshold.

Depth and thermal sensor fusion to enhance 3D thermographic reconstruction

Three-dimensional geometrical models with incorporated surface temperature data provide important information for various applications such as medical imaging, energy auditing, and intelligent robots. In this paper we present a robust method for mobile and real-time 3D thermographic reconstruction through depth and thermal sensor fusion. A multimodal imaging device consisting of a thermal camera and a RGB-D sensor is calibrated geometrically and used for data capturing. Based on the underlying principle that temperature information remains robust against illumination and viewpoint changes, we present a Thermal-guided Iterative Closest Point (T-ICP) methodology to facilitate reliable 3D thermal scanning applications. The pose of sensing device is initially estimated using correspondences found through maximizing the thermal consistency between consecutive infrared images. The coarse pose estimate is further refined by finding the motion parameters that minimize a combined geometric and thermographic loss function. Experimental results demonstrate that complimentary information captured by multimodal sensors can be utilized to improve performance of 3D thermographic reconstruction. Through effective fusion of thermal and depth data, the proposed approach generates more accurate 3D thermal models using significantly less scanning data.

High-frequency background modulation fringe patterns based on a fringe-wavelength geometry-constraint model for 3D surface-shape measurement

A new fringe projection method for surface-shape measurement was developed using four high-frequency phase-shifted background modulation fringe patterns. The pattern frequency is determined using a new fringe-wavelength geometry-constraint model that allows only two corresponding-point candidates in the measurement volume. The correct corresponding point is selected with high reliability using a binary pattern computed from intensity background encoded in the fringe patterns. Equations of geometry-constraint parameters permit parameter calculation prior to measurement, thus reducing measurement computational cost. Experiments demonstrated the ability of the method to perform 3D shape measurement for a surface with geometric discontinuity, and for spatially isolated objects.