Quick, easy, precise
3D machine vision is the future for robot vision. Many applications in robotics and automated serial production can only be satisfactorily implemented using three-dimensional data. This applies equally to challenging assembly processes such as bonding and welding, and to the notoriously tricky process of bin picking. The Ensenso 3D cameras from IDS represents a solution for 3D image capture that is impressively precise, cost-efficient and fast.
The Ensenso 3D cameras work according to the “projected texture stereo vision” principle. Each model has two integrated CMOS sensors and a projector that casts a high-contrast textures onto the object to be captured by using a pattern mask.
HOW DO ENSENSO CAMERAS WORK?
Ensenso cameras operate using Stereo Vision, which imitates the human vision. Two cameras acquire images from the same scene from two different positions.
Although the cameras see the same scene content, there are different object positions according to the cameras projection rays. Special matching algorithms compare the two images, search for corresponding points and visualize all point displacements in a Disparity Map.
Knowing the distance and viewing angle of the cameras in addition to the lens focal length, the Ensenso software converts these disparities in length units using the triangulation principle. So the 3D coordinates of each image pixel could be determined. The result is a 3D point cloud, which is the foundation for further applications based on 3D object information.
The matching process during the image comparison is based on contrast- and brightness graduations of the sensor pixels. So 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. The disparity cannot be uniquely determined. The result is an incomplete depth information of the scene.
Ensenso cameras improve the classic Stereo Vision principle by additional techniques to achieve a higher quality depth information and more precise measurement results. As a consequence Stereo Vision can be used in a wider range of applications.
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.
The FlexView technology can further improve the detail level of the disparity map of static scenes. The position of the pattern mask in the projection rays can be translated in small steps by a mechanical system using a piezoelectric actuator. The result is a varying texture on the object surface. Acquiring multiple image pairs with different textures of the same object scene produce a lot more image points. The resolution increases. The matching algorithm calculates significantly improved disparity maps by using all captured image pairs.
As a consequence of the texture displacement which produces additional structure information on glossy, dark or reflecting surfaces, the resolution and also the robustness of the resulting data will increase. A lot of processing algorithms benefit from the higher resolution and the lower noise. FlexView reduces post processing steps of the point cloud and further 3D processing time.
- The compact Ensenso cameras are perfectly suited for robot applications
- The EnsensoSDK includes the hand-eye-calibration for robots
- Optimized object localization by using CAD data
- Information about the positions of objects and obstacles enable high gripping speed by full motion planning
- Logistics automation
- Conveyor belt equipping
- Factory automation
- Storage systems
3D object reconstruction
Wood measuring with Ensenso cameras enables significant reduction potential in the wood industry.
- High accuracy in size classification and objective determination of the wood quality
- Early detection of object characteristics like structural damage or wood rot
- Determination of possible woodcutting for cost opimization
For this outdoor application the Ensenso cameras use the natural object textures (wood structure) which are produced by direct sunlight. (Passive Stereo Vision)
Object detection and classification
- Classification and counting by shape recognition
- Object identification enhancement by using aditional color information
- Sorting of defective parts by means of detected size deviations
- Size division by quick volume determination
- Quality assurance
The digitization of real body or shape dimensions is suitable for:
- Manufacturing of custom-fit shoe insoles or splints
- Orthopedic applications or services
Browse Models: https://en.ids-imaging.com/ensensofinder.html
For more information and pricing, contact: firstname.lastname@example.org