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Thermal imaging & vision systems


The system robustly segments unknown parcels, even under harsh lighting conditions.


Since the Stereo Vision quality directly depends on the scene’s light condition and object surface textures, finding and calculating coordinates of corresponding points on less textured or reflecting surfaces is very difficult. To meet these high requirements, Fizyrs first choice for customer solutions are camera models out of the IDS portfolio. Thereby the tasks are well divided: The uEye GigE CP camera is taking 2D images of objects and provides them as input for Fizyr’s algorithms, which then proceed to classify the objects under the camera. The objects can be unknown and varying in shape, size, colour, material, and stacking. Then the Ensenso camera creates the point cloud maps, Fizyr's software combines it with the information from the 2D image and analyses the surface of the cloud for suitable grasp poses for the gripper (or multiple grippers) and proposes the best ones. A clear representation of surfaces for different materials is critical, as it is a key component of its algorithms. No problem for the Ensenso 3D cameras from IDS: they improve the classic Stereo Vision principle by additional techniques to achieve a higher quality depth information and more precise measurement results. The specific camera models, as well as the


number of cameras per system, depend on the individual use case of the customer. For a typical bin-picking solution with a cobot, one Ensenso N35 is used in combination with a GigE uEye CP, but there are clients that use one Ensenso X36 and a GigE uEye CP for bin picking together with four Ensenso N35 cameras for stowing the item in other bins. The recommended uEye CP stands for


“Compact Power” and is the tiny powerhouse for industrial applications of all kinds. It offers maximum functionality with extensive pixel pre-processing and, thanks to the internal 120 MB image memory, is also perfectly suited for multi-camera systems. The camera delivers data at full GigE speed and enables single-cable operation up to 100 metres via PoE (“Power over Ethernet”).


Instrumentation Monthly April 2021


The Ensenso X36 3D camera system consists of a projector unit, two GigE uEye cameras either with 1.3 MP or 5 MP sensors (CMOS, monochrome), mounting brackets and adjustment angles, three lenses as well as sync. and patch cables to connect the camera with the projector unit. The FlexView technology ensures a better spatial resolution as well as a very high robustness of the system for dark or reflecting surfaces.


The selected UI-5240CP-C-HQ Rev. 2 is a


particularly powerful industrial camera with the e2v 1.3 megapixel CMOS sensor. This is one of the most sensitive sensors in the IDS product portfolio and apart from the here used color version it is available in monochrome and as NIR version. Besides its outstanding sensitivity to light, the camera also has a range of other distinctive features which makes the sensor extremely flexible if requirements or ambient conditions change. The compact and robust aluminum housing of the Ensenso N35 3D camera with lockable GPIO connector for trigger and flash and GigE connector, has two monochrome CMOS sensors (Global Shutter, 1280 x 1024 pixels) and a projector. Via Power over Ethernet a data transfer and power supply are possible with long cable lengths. Due to the integrated FlexView technology the N35 models are particularly suitable for 3D acquisition of still objects and for working distances up to 3,000 mm.


“Fizyr normally uses one camera per system,


usually uEye cameras in combination with Ensenso N35 and X36, but there are no limitations. The most common use case for Fizyr so far is one uEye and one Ensenso per system,” underlines Herbert ten Have, CEO at Fizyr. The different Ensenso models that can be


used have one thing in common: the dispose of a light-intensive projector produces a high- contrast texture on the object surface by using a pattern mask, even under difficult light conditions. The projected texture supplements the weak or non-existent object surface structure. Therefore this principle is also called “Projected Texture Stereo Vision”. The result is a more detailed disparity map and a more complete and homogeneous depth information of the scene. Their extensive features qualify both Ensenso 3D camera models for the wide range of demanding applications in the supply chain and logistics industry, such as 3D object recognition, classification and localisation (e.g. quality


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