Segmentation phase measuring deflectometry for measuring structured specular surfaces

Accurate and fast three-dimensional (3D) measurement for industrial products/components designed to possess 3D structured shapes is a key driver for improved productivity. However, challenges for current techniques are considerable to measure structured specular surfaces. A technique named segmentation phase measuring deflectometry (SPMD) is proposed in this paper, which enables structured specular surfaces to be measured with high accuracy in one setup. Concept of segmentation in topology is introduced into phase measuring deflectometry, which separates a surface with complex structures into continuous segments. Each segment can be reconstructed based on gradient information to achieve good form accuracy, and all reconstructed segments can be fused into a whole 3D strucutred form result based on their absolute spatial positioning data. Here, we propose and discuss the principle of SPMD, a segmentation technique to separate a strucured surface into segments, a spatial positioning technique to obtain absolute position of the segments, and a data fusion strategy to fuse all reconstructed segments. Experimental results show SPMD can achieve nanometer level accuracy for form measurement of continuous segments by comparing with stylus profilometer, which is significantly higher than the accuracy of direct phase measuring deflectometry. Meanwhile, SPMD has micron level spatial positioning accuracy for structures by measuring two specular steps and comparing with coordinate measuring machine, which differentiates this technique from gradient-based phase measuring deflectometry that extends measurement capability from continuous specular surfaces to complex structured specular surfaces. Compared with the existing measurement techniques, SPMD significantly improved the convenience and ability to measure freeform and structured specular surfaces with the advantages of high measurement accuracy, fast measurement, and potential application for embedded measurement.

3D Measurement of Structured Specular Surfaces Using Stereo Direct Phase Measurement Deflectometry

With the rapid development of modern manufacturing processes, ultra-precision structured freeform surfaces are being widely explored for components with special surface functioning. Measurement of the 3D surface form of structured specular objects remains a challenge because of the complexity of the surface form. Benefiting from a high dynamic range and large measuring area, phase measurement deflectometry (PMD) exhibits great potential in the inspection of the specular surfaces. However, the PMD is insensitive to object height, which leads to the PMD only being used for smooth specular surface measurement. Direct phase measurement deflectometry (DPMD) has been introduced to measure structured specular surfaces, but the surface form measurement resolution and accuracy are limited. This paper presents a method named stereo-DPMD for measuring structured specular objects by introducing a stereo deflectometor into DPMD, so that it combines the advantages of slope integration of the stereo deflectometry and discontinuous height measurement from DPMD. The measured object is separated into individual continuous regions, so the surface form of each region can be recovered precisely by slope integration. Then, the relative positions between different regions are evaluated by DPMD system to reconstruct the final 3D shape of the object. Experimental results show that the structured specular surfaces can be measured accurately by the proposed stereo-DPMD method.

Radiometric Calibration of an Inexpensive LED-Based Lidar Sensor

Radiometric calibration of laser-based, topographic lidar sensors that measure range via time of flight or phase difference is well established. However, inexpensive, short-range lidar sensors that utilize non-traditional ranging techniques, such as indirect time of flight, may report radiometric quantities that are not appropriate for existing calibration methods. One such lidar sensor is the TeraRanger Evo 60 m by Terabee, whose reported amplitude measurements do not vary smoothly with the amount of return signal power. We investigate the performance of a new radiometric calibration model, one based on a neural network, applied to the Evo 60 m. The proposed model is found to perform similarly to those applied to traditional lidar sensors, with root mean square errors in predicted target reflectance of no more than ±6% for non-specular surfaces. The radiometric calibration model provides a generic approach that may be applicable to other low-cost lidar sensors that report return signal amplitudes that are not smoothly proportional to target range and reflectance.