Error analysis and planning accuracy for dimensional measurement in active vision inspection

Yang, Christopher C.; Marefat, Michael M.; Ciarallo, Frank W.

doi:10.1109/70.678456

Cited by 35 publications

(15 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to limitations on the physical accuracy of positioning the cameras and the target object fixture, the true poses of these entities will inevitably differ, to some extent, from the optimized design [3], [23]. If the algorithm converges on a global optimum s, and the actual position achieved in practice is s e , then F (s e ) ≤ F (s).…”

Section: Compensating For Positioning Errormentioning

confidence: 96%

“…The scan density is the horizontal resolution of the range image, and can be thought of as a requirement on the "instantaneous" resolution at task point t. The height resolution is the precision to which range values are measurable at t; this is generally the most important requirement in an inspection task (see the discussion of quantization error by Yang et al [23]). The blur circle requirement is derived from the dependence of the profile interpolation algorithm on focus, usually discussed in the supplier's documentation.…”

Section: Task Requirementsmentioning

confidence: 99%

“…with z H (H, p) = Hh sin ζ(p) tan α l + tan α r (19) where ζ(p) is the view angle to p, as given by (23). (19) is derived similarly to (17), with the additional term accounting for the effect of the oblique angle of incidence.…”

Section: ) Occlusionmentioning

confidence: 99%

See 2 more Smart Citations

Semiautomatic Model-Based View Planning for Active Triangulation 3-D Inspection Systems

Mavrinac

Chen

Alarcon-Herrera

2015

IEEE/ASME Trans. Mechatron.

View full text Add to dashboard Cite

A semiautomatic model-based approach to the view planning problem for high-resolution active triangulation 3-D inspection systems is presented. First, a comprehensive, general, high-fidelity model of such systems is developed for the evaluation of configurations with respect to a model of task requirements, with a bounded scalar performance metric. The design process is analyzed, and the automated view planning problem is formulated only for the critically difficult aspects of design. A particle swarm optimization algorithm is applied to the latter portion, including probabilistic modeling of positioning error, using the performance metric as an objective function. The process leverages human strengths for the high-level design, refines low-level details mechanically, and provides an absolute measure of task-specific performance of the resulting design specification. The system model is validated, allowing for a reliable rapid design cycle entirely in simulation. Parameterization of the optimization algorithm is analyzed and explored empirically for performance.Index Terms-Inspection, laser scanner, range camera, view planning.

show abstract

Section: Compensating For Positioning Errormentioning

confidence: 96%

Section: Task Requirementsmentioning

confidence: 99%

See 1 more Smart Citation

Semiautomatic Model-Based View Planning for Active Triangulation 3-D Inspection Systems

Mavrinac

Chen

Alarcon-Herrera

2015

IEEE/ASME Trans. Mechatron.

View full text Add to dashboard Cite

show abstract

“…Image digitization may causes quantization errors, errors in range estimation are particularly caused by spatial quantization, and are within ±1/2 pixels [30], [31]. The results of range estimation are dominated by the projective v-coordinate of P 1 .…”

Section: Quantization Errorsmentioning

confidence: 99%

“…Let h denote the initially determined height, and h 2 denote the actual height. According to (26), the Z-coordinate mapping result can be obtained by (30). If the original height h is adopted, then the error coming from changes of height will be Z dh in (31) and the error ratio is e rh in (32).…”

Section: Influence Of Changes In Translationmentioning

confidence: 99%

Range and Size Estimation Based on a Coordinate Transformation Model for Driving Assistance Systems

Lin

Chen

2009

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYThis paper presents new approaches for the estimation of range between the preceding vehicle and the experimental vehicle, estimation of vehicle size and its projective size, and dynamic camera calibration. First, our proposed approaches adopt a camera model to transform coordinates from the ground plane onto the image plane to estimate the relative position between the detected vehicle and the camera. Then, to estimate the actual and projective size of the preceding vehicle, we propose a new estimation method. This method can estimate the range from a preceding vehicle to the camera based on contact points between its tires and the ground and then estimate the actual size of the vehicle according to the positions of its vertexes in the image. Because the projective size of a vehicle varies with respect to its distance to the camera, we also present a simple and rapid method of estimating a vehicle's projective height, which allows a reduction in computational time for size estimation in real-time systems. Errors caused by the application of different camera parameters are also estimated and analyzed in this study. The estimation results are used to determine suitable parameters during camera installation to suppress estimation errors. Finally, to guarantee robustness of the detection system, a new efficient approach to dynamic calibration is presented to obtain accurate camera parameters, even when they are changed by camera vibration owing to on-road driving. Experimental results demonstrate that our approaches can provide accurate and robust estimation results of range and size of target vehicles.

show abstract

Optimized sensor placement for active visual inspection

Yang

Ciarallo

2000

J. Robotic Syst.

View full text Add to dashboard Cite

This article presents an optimized sensor planning system for active visual inspection of three‐dimensional manufacturing computer‐aided design (CAD) models. Quantization errors and displacement errors are inevitable in active visual inspection. To obtain high accuracy for dimensioning the entities of three‐dimensional CAD models, minimization of these errors is essential. Spatial quantization errors result in digitization. The errors are serious when the size of the pixel is significant compared to the allowable tolerance in the object dimension on the image. In placing the active sensor to perform inspection, displacement of the sensors in orientation and location is common. The difference between observed dimensions obtained by the displaced sensor and the actual dimensions is defined as displacement errors. The density functions of quantization errors and displacement errors depend on camera resolution and camera locations and orientations. The sensor constraints, such as resolution, focus, field‐of‐view, and visibility constraints, restrict sensor placement. To obtain a satisfactory view of the targeted entities of the CAD models, these constraints have to be satisfied. In this article, we focus on the edge line segments as the inspected entities. We use a genetic algorithm to minimize the probabilistic magnitude of the errors subject to the sensor constraints. Since the objective functions and constraint functions are both complicated and nonlinear, traditional nonlinear programming may not be efficient and it may trap at a local minimum. Using crossover operations, mutation operations, and the stochastic selection in the genetic algorithm, trapping can be avoided. Experiments are conducted and the performance of the genetic algorithm is presented. Given the CAD model and the entities to be inspected, the active visual inspection planning system obtains the sensor setting that maximizes the probabilities of a required accuracy for each entity. © 2001 John Wiley & Sons, Inc.

show abstract

Error analysis and planning accuracy for dimensional measurement in active vision inspection

Cited by 35 publications

References 35 publications

Semiautomatic Model-Based View Planning for Active Triangulation 3-D Inspection Systems

Semiautomatic Model-Based View Planning for Active Triangulation 3-D Inspection Systems

Range and Size Estimation Based on a Coordinate Transformation Model for Driving Assistance Systems

Optimized sensor placement for active visual inspection

Contact Info

Product

Resources

About