How can 3D vision and point clouds improve industrial automation?

Point clouds should not be confused with real clouds. The real clouds are in the sky while the point clouds exist on the computer. While real clouds can produce rain, you should not expect rain from point cloud regardless how you end up using them

Points clouds should not be confused with meshes, which contain not only points but also vertexes (or edges, segments between the points) and faces (most of the time triangles) that add surface and structure information to the points. Point clouds are a set of points, not structured, not ordered. This adds some complexity (and therefore computation time) as every neighboors search, for example, has to loop over all the points.

In high level summary automation today can best be enabled by having accurate data including location and cube not to mention capability of unique item dimensions and precise spatial location data in at least X, Y and increasing in importance also Z to unlock more capabilities. Using hardware such as LiDAR and digital tools to build virtual maps gives you information on environment and these asset dimensions that allow for needed business outputs such as node movement, ASRS storage and retrieve, safe robotic travel, etc. Point clouds can allow for the outcomes though precise measurements in static and dynamic environments, production or non production.

Localization and spatial awareness continues to evolve as technology improves, creating new and unique solutions to drive process efficiency. 3D spatial learning with AR/VR is a good example of a new platform, enabled by tech, that can provide new modalities for teaching with significantly improved comprehension speeds (in some cases by close to 20%) and retention. Good for Walmart to be embracing these new innovations to improve productivity.

3D vision and point clouds enhance industrial automation by providing accurate spatial perception for robotic systems. They enable robots to perceive and interact with the environment in real-time, improving object recognition, localization, and manipulation tasks. Point clouds offer detailed representations of objects and scenes, facilitating tasks like quality control, bin picking, and assembly. This technology enhances efficiency, flexibility, and adaptability in manufacturing processes, ultimately boosting productivity and quality in industrial automation systems.

Point clouds can help automate tasks such as: - Creating 3D models of objects or spaces from scanned data, which can be used for design, simulation, or visualization. - Detecting and recognizing objects or features in point clouds, such as pipes, walls, spheres, or cylinders, which can be used for inspection, measurement, or classification purposes. - Comparing point clouds to detect changes or anomalies, such as damage, deformation, or movement, which can be used for monitoring, maintenance, or safety purposes.

Point clouds are revolutionizing industrial automation by leveraging data from various sensors. In manufacturing, they enhance quality control by identifying defects swiftly. In robotics, lidar-generated point clouds improve navigation and object recognition, optimizing workflows. In construction, these point clouds aid in site planning and structural monitoring, facilitating preventive measures. Integrated with algorithms, point clouds pave the way for data-driven decision-making, marking a paradigm shift in industrial automation towards enhanced precision and adaptability.

The post on UPS and GEEK collaborating on warehouse management and automated supply chain is really interesting. It opens up a spectrum of innovative thinking on all aspects of warehousing namely stacking and retrieval systems and forward integration to assembly units or to the distribution centers. That aspect of supply chain is what drags me to know more about this system

- Object Recognition: In the realm of automation, point clouds enable machines to recognize and identify objects with remarkable accuracy, fostering a more efficient and adaptable manufacturing process. - Navigation Prowess: Robots equipped with 3D vision powered by point clouds can navigate complex environments, avoiding obstacles and optimizing their path to enhance productivity.

I think the other important benefit of point clouds is the ability to track in real-time the human and control the robot according. This will provide a close collaboration between the human and the robot.

- Data Overload: The richness of point cloud data comes with its challenges. Processing and managing large volumes of data require robust computational power and efficient algorithms. - Real-time Constraints: Achieving real-time processing of point cloud data can be demanding, especially in scenarios where split-second decision-making is crucial.

In my experience the challenges of point clouds in industrial automation are complex. Firstly, real-time processing of large volumes of point cloud data demands high computational resources which impacts system performance. Secondly, point clouds often contain noise, occlusions, or missing data, posing challenges for accurate interpretation and analysis. To tackle these challenges, we need strong plans for sorting and cleaning the data before using it. Also, to make sure the systems work well, we have to calibrate the sensors properly and consider how the environment might affect them. It's really important to handle these issues well so that we can make the most out of point cloud technology in industrial automation.

Sensors can generate hundreds of thousands of points in a point cloud in a single rotation - resulting in large amounts of data to process, leading to problems with data processing and management.

Despite their undeniable potential, point clouds also present formidable challenges in the realm of industrial automation. Chief among these challenges is the management and processing of vast quantities of data inherent in point cloud representations. Ensuring real-time processing and interpretation of this data poses significant computational and algorithmic hurdles. Additionally, issues such as occlusions, noise, and varying environmental conditions can impede the accuracy and reliability of point cloud-based automation systems, necessitating robust solutions to mitigate these challenges effectively.

Point clouds pose challenges in data volume, requiring significant storage and processing capabilities. Managing noise and inaccuracies, often from sensor limitations, demands advanced filtering. Accurate registration of multiple point clouds, especially in dynamic scenes, can be challenging. Integration with existing workflows and interpretation of dense data also require expertise. Balancing these challenges with the benefits remains crucial for effective implementation.

- Precision and Accuracy: Point clouds offer unparalleled precision, allowing automation systems to operate with a level of accuracy that was once a distant dream. - Adaptability: The ability to capture the environment in 3D empowers automation to adapt swiftly to changes, ensuring seamless operations in dynamic settings.

Benefits of using a 3D point cloud in industrial automation: Accuracy: Point clouds are extremely precise, making them ideal for tasks such as quality inspection and robot control. Efficiency: Point clouds can be generated and processed quickly, making them an efficient tool for task automation. Safety: Point clouds can be used to create 3D models of hazardous environments, allowing engineers and technicians to perform simulations and tests without risking their safety. Flexibility: Point clouds can be used with a wide range of software and hardware, making them a versatile tool for industrial automation.

3D random bin picking in general requires less operator intervention to load parts. Since 3D vision can pick from a volume instead of a flat plane we can stage more parts in a smaller space. 2D vision often has a limited field of view if you want to maintain accuracy of your picks so 2D vision can take up more space. With 2D a vibratory feeder can be added but in some cases parts are susceptible to damage from vibration. As well cost wise adding a vibratory feeder to a 2D system can end up being as expensive in total as a 3D system. Some 3D systems are also less susceptible to ambient light issues and better at seeing low contrast and shiny materials than 2D. As well 3D bin picking systems require minimal set up time.

The benefits afforded by point clouds in industrial automation are manifold and profound. Foremost among these benefits is the unparalleled level of spatial awareness and detail offered by point cloud representations, enabling precise and robust localization, navigation, and manipulation tasks for robotic systems. Furthermore, point clouds facilitate comprehensive environmental perception, empowering automation systems to adapt dynamically to complex and dynamic surroundings. Additionally, the rich data inherent in point clouds facilitates advanced analytics and decision-making processes, enhancing operational efficiency and driving innovation within industrial settings.

Point clouds offer significant benefits for industrial automation, including enhanced performance, accuracy, and reliability of robots. They provide precise 3D representations, enabling better manipulation and recognition, and improve operator safety by detecting obstacles and hazards. Point clouds optimize workflows, increasing efficiency and productivity, and support new applications like quality control, inspection, mapping, and navigation. They reduce costs by minimizing errors and rework, save time through faster and more reliable processes, and reduce material waste by ensuring precise cuts and assemblies. These advantages make point clouds ideal for industrial automation.

It would be good to have a global 3D point cloud for mapping functions using lidar-equipped infrastructure. Robots can then subscribe wirelessly to the crowd-sourced point cloud using a standard API instead of having a lidar in each machine, similar to GPS or cloud RTK solutions used for localization functions today. This would be analogous to providing ambient lighting inside a building instead of expecting everyone to carry a flashlight.

AI powered by Deep Large Learning Models are making a significant impact to all walks of life including manufacturing. AI models would love point cloud data for many applications such as anomaly, classification, segmentation etc, liking of point cloud data to AI models is the next biggest technological challenge and a trends

The future of point clouds is marked by increased integration with AI and machine learning, making data processing more efficient. Advanced algorithms will handle larger datasets, addressing storage and processing challenges. Expect point clouds to play a vital role in real-time applications, especially in augmented and virtual reality. As sensors advance, broader applications in autonomous vehicles, urban planning, and environmental monitoring will unfold. Standardization efforts will improve interoperability, unlocking new possibilities across diverse fields.

Neural Radiance Fields (NeRFs) are transforming 3D modeling, vital for point clouds in industrial automation. NeRFs use deep learning to create detailed 3D representations from 2D images, offering more precision than traditional point clouds. They enable realistic simulations, improving processes like quality control and predictive maintenance. By integrating virtual and real-world data, NeRFs enhance planning and decision-making in automation, reconstructing intricate details and textures efficiently, thus elevating the effectiveness of automation systems. For more information, check out the research of Preston Culbertson, especially "Vision-Only Robot Navigation in a Neural Radiance World."

Of course point clouds are a good entry point to providing robots with deeper understanding of its environment, however raw data on itself isn’t of much use. We mastered object recognition and position extraction a long time ago. I am very excited to see what will future research bring in the area of “cognitive” robotics - we already have path planning and grip position calculation, but it will be exciting to see how far we can push robotics in understanding its surroundings and acting based on what it senses, without too much of explicit programing. Exciting times! ????

3D Vision gives tremendous opportunity to manufacturing and robotics. Being able to get a better understanding of the environment in 3d dimensions allows for more processes and tasks to be automated as well as interact in environments with mobile robotics and systems. It's arguable that being able to get 3D information is one of the biggest advancements in robotics in many years.

3D technologies like Lidar and scanning cameras are amazing at analyzing unsorted parts which allows factories to spend more time manufacturing and less time packaging. Bin picking oddly shaped parts with a robot would not be possible otherwise. Expanding on this, the ability to quickly detect the position and dimensions of anything weather in a fix spot or on a conveyor greatly improves the efficiency of any QC operation.

Yes I totally agree. Robotics has been revolutionized by the advancement in Vision technology. Allowing significantly higher levels of productivity. And decreasing the amount of “Hard tooling” required for accurate placement. Even redeployment to new applications has been enhanced by allowing the Vision System to drive the robot. Thanks for posting!

3D vision offers enhancements to industrial automation by providing more precise positional data essential for jobs like object manipulation and guidance. Furthermore, 3D sensors facilitate quality control through surface defect inspection & dimensioning. Todays 3D sensors also offer longer working distances that complement the work envelope of a robot cell.

Efficient Material Handling and Logistics: In logistics, 3D vision facilitates the automation of loading, unloading, sorting, and tracking of goods. By accurately measuring volumes and dimensions, it optimizes space utilization in storage and transportation.