Picking the Unknown: Flexible Automation in Logistics

Picking the Unknown Flexible Automation in Logistics

The global pandemic has radically impacted the supply chain and logistics industry, making the need for robotic automation more urgent than ever. With more than 70% of labour in warehousing being dedicated to picking and packing, numerous companies are gradually investing in logistics automation.

But what happens when the robots must handle an unlimited number of unknown stock keeping units? These companies need a fast, reliable and robust way to automate picking and placing of a large variety of objects.

This challenge was taken up successfully by the Dutch company Fizyr. The computer vision company based in Delft focuses on enabling robots to pick unknown objects even in harsh logistics environments. The result is an automated vision solution that enables logistic automation in various conditions and applications, like item picking, parcel handling, depalletizing, truck unloading or baggage handling.

Instead of providing a proprietary picking cell, like most players in the market do, Fizyr has created a plug-and-play modular software product that integrates smoothly with any system, giving integrators the freedom to choose the best hardware (for example, cobots or industrial robots) for their picking cell.

While facilitating cost, robustness and speed optimisation, Fizyr has partnered with IDS for the cameras. Depending on the individual customer application, up to four Ensenso 3D cameras in combination with GigE uEye CMOS cameras are used.

An Ensenso 3D camera from IDS

An Ensenso 3D camera from IDS.


The integrated IDS industrial cameras ensure a reliable, precise image capture that is needed for Fizyr’s software algorithms which provide over 100 grasp poses each second, including the classification to handle objects differently. The software also performs quality control and detects defects to prevent damaged items from being placed on a sorter.

The algorithms are able to provide all relevant information about segmentation as well as about classification of type of parcel (including box, bag, envelope/flat, tube, cylinder, deformable, and so on).

The system recognises outliers or non-conveyables (that is, damaged goods), best possible grasp poses in six degrees of freedom and multiple ordered poses per object. It allows sensors or robots to deal with closely stacked or overlapping objects, highly reflective items and apparel in polybags, white-on-white and black-on-black flats, as well as transparent objects.

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 requirements, Fizyr uses the uEye GigE CP camera to take 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. The Ensenso 3D camera then creates the point cloud maps, and 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.


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

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


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.

“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,” said Herbert ten Have, CEO at Fizyr.


The strong growth of e-commerce has increased the demand for larger packages. At the same time, the constraints imposed by the global pandemic have led both to a reduction in the available labour force and to an urgent need for physical distance in companies.

To meet these challenges, companies are increasingly turning to robotic automation, in which artificial intelligence and machine vision play a crucial role. At the same time, this reduces costs and increases occupational safety.

The demand for high-quality modules for state-of-the-art automated picking cells is growing worldwide. System integrators’ requirements for software and hardware components are increasing in equal measure. The Fizyr-IDS solution offers the following advantages:

  • As a plug-and-play solution, it enables easy integration into any system.
  • It is hardware-agnostic and thus enables customers to flexibly adapt their order picking system to their needs at any time.
  • It includes a robust, precise, flexible and easy-to-use 3D vision system that provides optimal object information at all times.

Let’s discuss how we can help you

We’re happy to chat to see how we can help solve your problems best. Schedule to pick our brains or request a demo.

Originally published on processonline.com

Logistics, AI, and robotics news curated by industry experts. Delivered to your inbox once a month.

Subscribe to newsletter