Multi-sensor CMM measurement of freeform objects with sharp features

Coordinate measuring machines (CMMs) are one of the most important geometrical measurement devices which are widely used in various industrials. The development of high precision, reliable and high speed trigger probe enables automatic and accurate measurement of freeform surfaces, which leads the trigger probe to be the most widely used probing system in the CMM machines. However, the traditional probing system has its limitations. For example, it can only measure points on a relatively smooth surface. When measuring sharp features (e.g. edges, corners), the probing error is large. Moreover, it is difficult to probe on these features. This paper presents a multi-sensor CMM measurement method for measuring freeform shapes with sharp features, which utilizes the trigger probe to measure the points on the smooth surface while uses the image sensor to measure the sharp features. Experiment was conducted on a multi-sensor CMM machine to demonstrate the effectiveness of the proposed method. The enhancement of the accuracy with the proposed method was also discussed. The result proved that the proposed method can significantly improve accuracy for measurement of the sharp features

Camera calibration from road lane markings

Three-dimensional computer vision techniques have been actively studied for the purpose of visual traffic surveillance. To determine the 3-D environment, camera calibration is a crucial step to resolve the relationship between the 3-D world coordinates and their corresponding image coordinates. A novel camera calibration using the geometry properties of road lane markings is proposed. A set of equations that computes the camera parameters from the image coordinates of the road lane markings and lane width is derived. The camera parameters include pan angle, tilt angle, swing angle, focal length, and camera distance. Our results show that the proposed method outperforms the others in terms of accuracy and noise sensitivity. The proposed method accurately determines camera parameters using the appropriate camera model and it is insensitive to perturbation of noise on the calibration pattern.published_or_final_versio

LASER RANGE IMAGING FOR ON-LINE MAPPING OF 3D IMAGES TO PSEUDO-X-RAY IMAGES FOR POULTRY BONE FRAGMENT DETECTION

A laser ranging image system was developed for on-line high-resolution 3D shape recovery of poultry fillets. The range imaging system in conjunction with X-ray imaging was used to provide synergistic imaging detection of bone fragments in poultry fillets. In this research, two 5 mW diode lasers coupled with two CCD cameras were used to produce 3D information based on structured lights and triangulation. A laser scattering phenomenon on meat tissues was studied when calculating the object thickness. To obtain the accurate 3D information, the cameras were calibrated to correct for camera distortions. For pixel registrations of the X-ray and laser 3D images, the range imaging system was calibrated, and noises and signal variations in the X-ray and laser 3D images were analyzed. Furthermore, the relationship between the X-ray absorption and 3D thickness of fillets was obtained, and a mapping function based on this relationship was applied to convert the fillet 3D images into the pseudo-X-ray images. For the on-line system implementation, the imaging hardware and software engineering issues, including the data flow optimization and the operating system task scheduling, were also studied. Based on the experimental on-line test, the range imaging system developed was able to scan poultry fillets at a speed of 0.2 m/sec at a resolution of 0.8(X) x 0.7(Y) x 0.7(Z) mm3. The results of this study have shown great potential for non-invasive detection of hazardous materials in boneless poultry meat with uneven thickness

Integrated tactile-optical coordinate measurement for the reverse engineering of complex geometry

Complex design specifications and tighter tolerances are increasingly required in modern engineering applications, either for functional or aesthetic demands. Multiple sensors are therefore exploited to achieve both holistic measurement information and improved reliability or reduced uncertainty of measurement data. Multi-sensor integration systems can combine data from several information sources (sensors) into a common representational format in order that the measurement evaluation can benefit from all available sensor information and data. This means a multi-sensor system is able to provide more efficient solutions and better performances than a single sensor based system. This thesis develops a compensation approach for reverse engineering applications based on the hybrid tactile-optical multi-sensor system. In the multi-sensor integration system, each individual sensor should be configured to its optimum for satisfactory measurement results. All the data measured from different equipment have to be precisely integrated into a common coordinate system. To solve this problem, this thesis proposes an accurate and flexible method to unify the coordinates of optical and tactile sensors for reverse engineering. A sphere-plate artefact with nine spheres is created and a set of routines are developed for data integration of a multi-sensor system. Experimental results prove that this novel centroid approach is more accurate than the traditional method. Thus, data sampled by different measuring devices, irrespective of their location can be accurately unified. This thesis describes a competitive integration for reverse engineering applications where the point cloud data scanned by the fast optical sensor is compensated and corrected by the slower, but more accurate tactile probe measurement to improve its overall accuracy. A new competitive approach for rapid and accurate reverse engineering of geometric features from multi-sensor systems based on a geometric algebra approach is proposed and a set of programs based on the MATLAB platform has been generated for the verification of the proposed method. After data fusion, the measurement efficiency is improved 90% in comparison to the tactile method and the accuracy of the reconstructed geometric model is improved from 45 micrometres to 7 micrometres in comparison to the optical method, which are validated by case study

A gaussian process-based multi-sensor metrology system for precision measurement of freeform surfaces

Nowadays, precision freeform surfaces play an important role since they have superior performance and indispensable functionalities. Due to their geometrical complexity, high form accuracy and low surface roughness, precision freeform surfaces introduce a lot of research challenges in precision manufacturing and measurement processes. This is particularly true when the measurement is performed on traditional off-line single-sensor instruments such as white light interferometers (WLIs) and coordinate measuring machines (CMMs) whose measurement abilities are limited. For a single-sensor instrument, the measurement range and measurement resolution always need to strike a balance since the two terms appear to be contradictory. Moreover, when the workpiece is extremely large and error compensation procedure is needed to correct the form error of the workpiece, it is necessary to perform the measurement on machining facilities since repositioning error is unacceptable. However, off-line based measurement instruments cannot fulfil the in-situ measurement requirement. To address the above issues, this research firstly established a generic Gaussian process data modelling and image registration-based stitching method for the measurement of precision freeform surfaces based on traditional single-sensor surface measurement instruments using multiple measurement methods. With the proposed method, a dataset with a large measurement range and high resolution can be obtained. The proposed stitching method provides a turn-key solution for high dynamic range measurement using single-sensor instruments with a multiple measurement method. For multi-sensor instruments such as multi-sensor coordinate measuring machines (CMMs), this study proposes a Gaussian process-based data modelling and maximum likelihood data fusion method for the measurement of freeform surfaces for multi-sensor CMMs. The method utilizes an optical sensor such as laser sensor and a touch trigger probe mounted on the multi-sensor coordinate measuring machine for the measurement of freeform surfaces, and the measurement data are modelled using the Gaussian process modelling method. The combination of different kinds of sensors balances the measurement efficiency and accuracy since most optical sensors have a fast measurement speed and high density but low accuracy while contact sensors have an accurate measurement result but low efficiency. The measurement datasets from the laser sensor and touch trigger probe were fused with a maximum likelihood method so as to reduce the overall measurement uncertainty. To address the in-situ measurement issue, this thesis proposes an autonomous multi-sensor in-situ metrology system for high dynamic range measurement of freeform surfaces for precision machine tools. The system utilizes a laser scanner and a motion sensor together with a designed trajectory so as to perform in-situ measurement on the machining facilities. The proposed system is independent of the machining facilities which makes it extendable to a wide range of industrial applications. Based on the theory developed for the autonomous multi-sensor in-situ metrology system, a homogeneous multi-sensor in-situ measurement metrology system was developed equipped with a laser line sensor and laser point sensor. The laser line sensor provides high lateral resolution data while the laser point sensor gives accurate data. The measurement data from these two kinds of sensors are fused to obtain a more accurate result without losing the high lateral resolution. The present study has very large potential applications in industry. The successful development of the Gaussian process and image registration-based stitching method provides an important means for high dynamic range measurement, while the Gaussian process-based data modelling and maximum likelihood-based data fusion method establishes a generic measurement strategy for multi-sensor coordinate measuring machines so as to improve the measurement accuracy for precision freeform surfaces. The proposed in-situ multi-sensor high dynamic range measurement method and hence the homogeneous multi-sensor in-situ metrology system enable the measurement ability of machine tools so as to improve the efficiency and accuracy of the precision manufacture of complex freeform surfaces. The outcome of the research contributes significantly to the measurement science and technology, especially in the field of multi-sensor measurement and in-situ measurement of precision freeform surfaces