Real-time 3D shape inspection system of automotive parts based on structured light pattern

Xu, Jing; Xi, Ning; Zhang, Chi; Shi, Quan; Gregory, John

doi:10.1016/j.optlastec.2010.04.008

Cited by 93 publications

(40 citation statements)

References 11 publications

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“…Metrology is also commonly conducted in industrial and manufacturing settings, in which the shape of components and equipment is inspected for damage or quality assessment [34,48,84,116]. Depending on customer requirements, as well as national and international industry standards, a high-quality depth sensor is typically needed to measure the shape of an engineered object in order to compare with the corresponding CAD to test the accuracy, precision, reliability, and traceability of production.…”

Section: Motivationmentioning

confidence: 99%

Optimal 6D Object Pose Estimation with Commodity Depth Sensors

Landau

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Accurate 6D object pose estimation, as well as other model-based shape matching objectives such as object detection, classification, and shape inspection, is prominent and necessary in a large number of domains and applications. This includes household and robotics applications to automate various tasks, where input depth measurements are aligned to a corresponding model of the object, such as a CAD that is composed of several interconnected parts, depending on the model's level of detail and complexity. Industrial metrology systems also compare the shapes of engineered components and equipment to their corresponding 3D models for quality assessment and anomaly detection; this often requires very costly and high resolution projection and detection mechanisms. Structuredlight laser scanners have, however, emerged as a viable option to provide cheap and reasonably accurate depth estimates, which have recently become commodity-priced within this decade due to the maturity of the technology. These sensors are also robust for semi-transparent and reflective surfaces, dynamic scenery, ambient background light, and temperature drift. Certain structured-light light coding methods unfortunately need to implement a complicated, non-invertible transformation of a distorted light pattern to generate the pixel-by-pixel depth estimates, thereby leading to a loss of information. This can result in poorly estimated or even missing depth data, especially when the object has small 3D features or non-ideal surface properties. Moreover, the informative rigid body constraint is blurred when the nonlinear 2D depth image to 3D pseudo-measurement point cloud transformation is applied, which is the domain where most model-based shape matching methods operate.Alternatively, estimating pose from information early in the structured-light sensing/processing chain has the potential to alleviate errors induced in subsequent nonlinear processing steps that are in fact locally scene dependent. This motivates one of the main contributions from this dissertation, which presents an asymptotically optimal maximum likelihood estimation method that operates directly on the raw output IR images. This is made possible by an extensive study on a commonly utilized commodity-priced structured-light sensor, which contributed to the proposed high-fidelity IR and depth image predictor and simulator that models the physics of the transmit and receive optics,

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Section: Motivationmentioning

confidence: 99%

Optimal 6D Object Pose Estimation with Commodity Depth Sensors

Landau

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“…Traditional 2D vision is not applicable to inspect the object with free surface such as the turbine blade. 3D vision provides an alternative for these problems with more flexibility while little cost added, including various technologies [42], which is a powerful tool in industrial quality inspection [43], welding [44], lens quality inspection [45], thin-walled components inspection [46] and suitable for sorts of working conditions [47][48][49].…”

Section: Pilot Study: 3d Iqismentioning

confidence: 99%

Towards zero-defect manufacturing (ZDM)—a data mining approach

Wang

2013

Adv. Manuf.

114

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The quality of a product is dependent on both facilities/equipment and manufacturing processes. Any error or disorder in facilities and processes can cause a catastrophic failure. To avoid such failures, a zero-defect manufacturing (ZDM) system is necessary in order to increase the reliability and safety of manufacturing systems and reach zero-defect quality of products. One of the major challenges for ZDM is the analysis of massive raw datasets. This type of analysis needs an automated and self-organized decision making system. Data mining (DM) is an effective methodology for discovering interesting knowledge within a huge datasets. It plays an important role in developing a ZDM system. The paper presents a general framework of ZDM and explains how to apply DM approaches to manufacture the products with zero-defect. This paper also discusses 3 ongoing projects demonstrating the practice of using DM approaches for reaching the goal of ZDM.

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“…For accurate, reliable and fast measurements of industrial parts (e.g., automotive parts), the projection pattern is supposed to meet the following requirements: (a) high robustness to the reflectivity variance of the measured part; (b) high consistence of the measurement performance; (c) appropriate point cloud density to represent the 3D shape; (d) accurate location of the primitives; (e) rapid decoding capability. Motivated by these facts, we developed two novel structured light patterns for rapid 3D measurement (Xu et al, 2010;2011), inspired by previous research. The first one, X-point pattern, is a one-shot pattern based on geometrical feature and neighboring information.…”

Section: Related Workmentioning

confidence: 99%

Active Stereo Vision for 3D Profile Measurement

Xu¹

2011

Advances in Stereo Vision

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Real-time 3D shape inspection system of automotive parts based on structured light pattern

Cited by 93 publications

References 11 publications

Optimal 6D Object Pose Estimation with Commodity Depth Sensors

Optimal 6D Object Pose Estimation with Commodity Depth Sensors

Towards zero-defect manufacturing (ZDM)—a data mining approach

Active Stereo Vision for 3D Profile Measurement

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