Active Laser Triangulation Research Articles

System of technical vision based on active laser triangulation method

The number of failures, the percentage of faulty electric locomotives, and the number of unplanned repairs remain high at the enterprises for repair of traction rolling stock of the network of the main-line Russian railways. It was found that the main reasons for this situation are the unsatisfactory quality of current repairs and maintenance, the insufficient level of mechanization and automation of labor-intensive repair processes. The paper considers an existing positive example of the use of robotic equipment in assembly production for the repair of traction rolling stock. The analysis and classification of sensor control devices for industrial robots is performed. A method for controlling the driving devices of the repair equipment is proposed by obtaining a spatial-geometric image of the object’s surface before the beginning of technological processing by the method of active laser triangulation scanning. The principle of the action of three-dimensional scanning is described; a number of equipment with its technical characteristics and software used during the research in the laboratory of the university is listed. The result of the experiment of the proposed method on the model of the truck frame of the electric locomotive is presented. The metrological characteristics of the measurement results are calculated, allowing comparing and assessing the accuracy of the model obtained and the mock-up of the rolling stock truck. Based on the results of these calculations, it can be said with certainty that all errors and deviations of the results from true values lie within the limits of the norm and this technology can be used to design a vision system for robotic equipment.

ПРИМЕНЕНИЕ МЕТОДА АКТИВНОЙ ЛАЗЕРНОЙ ТРИАНГУЛЯЦИИ ДЛЯ РЕШЕНИЯ ЗАДАЧ АВТОМАТИЗАЦИИ ТЕХНОЛОГИЧЕСКИХ ПРОЦЕССОВ РЕМОНТА ПОДВИЖНОГО СОСТАВА

ПРИМЕНЕНИЕ МЕТОДА АКТИВНОЙ ЛАЗЕРНОЙ ТРИАНГУЛЯЦИИ ДЛЯ РЕШЕНИЯ ЗАДАЧ АВТОМАТИЗАЦИИ ТЕХНОЛОГИЧЕСКИХ ПРОЦЕССОВ РЕМОНТА ПОДВИЖНОГО СОСТАВА

The night vision distance measurement method dedicated for mobile robots

Abstract The paper presents a vision based visual servoing system and active laser triangulation method for distance measurement. The proposed hybrid system is dedicated to mobile robot operating in difficult lighting conditions. The problems caused by the use of the video system in extremely underexposured 3D scene have been discussed and two devices performing the distance measurement together with an analysis measurements have been analyzed.

Limits of Active Laser Triangulation as an Instrument for High Precision Plant Imaging

Laser scanning is a non-invasive method for collecting and parameterizing 3D data of well reflecting objects. These systems have been used for 3D imaging of plant growth and structure analysis. A prerequisite is that the recorded signals originate from the true plant surface. In this paper we studied the effects of species, leaf chlorophyll content and sensor settings on the suitability and accuracy of a commercial 660 nm active laser triangulation scanning device. We found that surface images of Ficus benjamina leaves were inaccurate at low chlorophyll concentrations and a long sensor exposure time. Imaging of the rough waxy leaf surface of leek (Allium porrum) was possible using very low exposure times, whereas at higher exposure times penetration and multiple refraction prevented the correct imaging of the surface. A comparison of scans with varying exposure time enabled the target-oriented analysis to identify chlorotic, necrotic and healthy leaf areas or mildew infestations. We found plant properties and sensor settings to have a strong influence on the accuracy of measurements. These interactions have to be further elucidated before laser imaging of plants is possible with the high accuracy required for e.g., the observation of plant growth or reactions to water stress.

Structured light-based shape measurement system

It is very important to acquire accurate depth information of target object or scene for many applications in machine learning. The use of 3D reconstruction based on active laser triangulation technology is very complex in real application. One main problem is that most of these technologies detect light stripes by considering each column or row of the image as independent signals causing lack of robustness. In real application, variable illumination, uneven surface and imaging noise will make stripe detection fail. In this paper, by considering laser stripe distortion assumption, we adopt efficient belief propagation algorithm to extract center of laser stripe, which proves superior to existing peak detection approaches. Because of occlusion and low reflectivity, laser stripe captured by the sensor will be cut into several parts at some points, which are referred to as outliers. As for non-outlier, the SNR of that point is high and the disparity difference between left and right neighbor is slight. First, determine whether a point is an outlier or not by computing the weighted SNR and disparity difference. Then efficient belief propagation algorithm is adopted to infer the outlier map which is called labels in machine learning. Experimental results demonstrate the feasibility of our proposed approach.

Fast and robust laser stripe extraction for 3D reconstruction in industrial environments

The use of 3D reconstruction based on active laser triangulation techniques is very complex in industrial environments. The main problem is that most of these techniques are based on laser stripe extraction methods which are highly sensitive to noise, which is virtually inevitable in these conditions. In industrial environments, variable luminance, reflections which show up in the images as noise, and uneven surfaces are common. These factors modify the shape of the laser profile. This work proposes a fast, accurate, and robust method to extract laser stripes in industrial environments. Specific procedures are proposed to extract the laser stripe projected on the background, using a boundary linking process, and on the foreground, using an improved Split-and-Merge approach with different approximation functions including linear, quadratic, and Akima splines. Also, a novel procedure to automatically define the region of interest in the image is proposed. The real-time performance of the proposed method is analyzed by measuring the time taken by the tasks involved in their application. Finally, the proposed extraction method is applied to two real applications: 3D reconstruction of steel strips and weld seam tracking.

Design and implementation of a 3-D mapping system for highly irregular shaped objects with application to semiconductor manufacturing

The basic technology for a robotic system is developed to automate the packing of polycrystalline silicon nuggets into fragile fused silica crucible in Czochralski (melt pulling) semiconductor wafer produc- tion. The highly irregular shapes of the nuggets and the packing con- straints make this a difficult and challenging task. It requires the delicate manipulation and packing of highly irregular polycrystalline silicon nug- gets into a fragile fused silica crucible. For this application, a dual optical 3-D surface mapping system that uses active laser triangulation has been developed and successfully tested. One part of the system mea- sures the geometry profile of a nugget being packed and the other the profile of the nuggets already in the crucible. A resolution of 1 mm with 15-KHz sampling frequency is achieved. Data from the system are used by the packing algorithm, which determines optimal nugget placement. The key contribution is to describe the design and implementation of an efficient and robust 3-D imaging system to map highly irregular shaped objects using conventional components in context of real commercial manufacturing processes. © 2002 Society of Photo-Optical Instrumentation Engi- neers. (DOI: 10.1117/1.1474438)

Robotic Manipulation of Highly Irregular Shaped Objects: Application to a Robot Crucible Packing System for Semiconductor Manufacture

The basic technology for a robotic system is developed to automate the packing of polycrystalline silicon nuggets into a fragile fused silica crucible in Czochralski (melt pulling) semiconductor wafer production. The highly irregular shapes of the nuggets and the packing constraints make this a difficult and challenging task. To address this task, key research areas are identified, developed, and integrated. In this system, nuggets are grasped by a three-cup suction gripper and manipulated with a seven-degree-of-freedom SCARA manipulator. An optical 3-D vision system, based on active laser triangulation, measures nugget and crucible profiles. A model-free Virtual Trial and Error packing algorithm determines optimal nugget placement in real time. A hybrid position-force control scheme has been implemented and tested for physical nugget placement. The simulation and laboratory tests show that the system has the capabilities of meeting high production rates, achieving high process constraints, and maintaining cost effectiveness that exceed levels obtained with manual packing. The results suggest that model-less robotic sensor control systems can be effective in manufacturing applications. The key contribution of this paper is to show that robot systems can be effectively used to manipulate highly irregular shaped objects in the context of real commercial manufacturing processes.

Outlier removal and discontinuity preserving smoothing of range data

Algorithms for the segmentation and description of range images are very sensitive to errors in the source data caused by noise processes in the optoelectronic sensing, and outliers caused by incorrect signal detection, for example false peaks in an active laser triangulation system. The authors present an approach to range data processing designed to reconstruct the underlying shape of the surfaces in the scene, yet preserve the discontinuities between them. The approach has two stages, first outlier removal by a lower complexity variation of the least median of squares estimator, and second, robust smoothing by anisotropic diffusion. To evaluate the proposed methods, the authors quantify the improvement in depth, normal and curvature estimation, and show how preprocessing improves surface patch segmentation and classification.