UNIDO’s Post

Lidar scanning #solutions have never been more #accessible! With this throwback to General Laser’s participation in the #ICONS2024 with the International Atomic Energy Agency (IAEA) and the Long Night of Research with the UNIDO Digital Transformation & AI Strategies at the United Nations Office at Vienna, we show how #tech breakthroughs can be in sync with #userfriendly design, in both #hardware & #software. GLidar is a truly mighty #laser scanner, with a sampling rate of up to 5.2 million points per seconds and unique ability to deliver instant results, no post processing. Yet, we built it in a way that cutting-edge technical advancement does not hinder the simplicity & elegance of use. Just open the case, attach a Makita battery, connect to the #GLidar’s hotspot with your mobile device, choose your scanning pre-sets, and off you go! Invest in the functionality of your team, making fieldwork of point cloud generation a task that any member of your team can handle. #unitednations #un #laser #laserscanner #LiDAR #3Dmap #digitalmapping #3Dscanning #precisionmapping #digitaltwin #pointcloud #GIS #engineering #infrastructureinspection #infrastructuremapping #datavisualization #construction #urbanplanning #tech #innovation #GeneralLaser #bim

Laser scanning is on a path to become more powerful, more useful, and more affordable — impacting a vast array of industries. Following a similar trajectory to computing, the technology is simply becoming smaller , cheaper and easier to use. 💥 Here are some important facts: LiDAR technology, especially in mobile form, is transforming laser scanning by enabling more accessible and effective scanning methods. Mobile LiDAR, powered by Simultaneous Localization and Mapping (SLAM) technology, allows for mapping while navigating unknown environments, utilizing various sensors including LiDAR for precision. Handheld scanners from brands like GeoSlam and NavVis simplify data collection, while drones with SLAM scanners are increasingly crucial for aerial mapping despite existing operational limitations. These advancements democratize point cloud data, benefiting engineering, urban planning, and scientific projects. Strategies for adopting new scanning techniques often begin with SLAM due to its affordability and fewer regulatory constraints, but integrating SLAM data with other sources is crucial for project accuracy and detail. ⁠The surge in 3D point clouds necessitates cloud-based data management and processing, offering enhanced accessibility, scalability, and collaboration. Cloud storage enables seamless sharing and flexible capacity, akin to Building Information Modelling (BIM). Cloud-based processing revolutionizes laser scanning workflows, with faster algorithms and parallel computing for accelerated and robust outcomes. ⁠Multimodal scans combine scanning, sensor, and visual data for a comprehensive view. They enhance detail and coverage, addressing challenges like inaccessible areas or diverse data needs, crucial for large-scale projects. To adopt multimodal scanning, explore innovative techniques and cloud-based registration software to streamline data integration. Ensure your tools can effectively combine datasets for a unified output. ⁠AR/VR integration with laser scanning technology is shaping the industry's future, offering new opportunities for innovation and application. Common applications include reviewing scan data with stakeholders and consumer products like Apple's iPhone with built-in LiDAR scanners. As AR/VR technologies advance, they hold significance across various sectors, from construction to retail. To adopt these technologies, consider incorporating laser scanning output into training and safety programs. Embrace the possibilities enabled by this evolving technology landscape. As you know, the future is here and at UNIQ Solutions we are sure that staying ahead of the curve is key in the evolving industry. To read the full blog please click the following link https://lnkd.in/eZbyrm5r #InsightfulGraphics #ArchitectureQuotes #DesignStatistics #InnovationInspiration #UniqDimensionsKnowledge

Revolutionize transportation infrastructure with 𝗖𝘆𝘃𝗹.𝗮𝗶's data-driven solutions! 𝗖𝘆𝘃𝗹.𝗮𝗶, born from the vision of co-founder 𝗗𝗮𝗻𝗶𝗲𝗹 𝗣𝗲𝗹𝗮𝗲𝘇, is transforming the transportation infrastructure sector through advanced digital tracking. With a recent $6 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝗶𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁, this 𝗕𝗼𝘀𝘁𝗼𝗻-𝗮𝗿𝗲𝗮 𝘀𝘁𝗮𝗿𝘁𝘂𝗽 leverages lidar, cameras, and sensors combined with geospatial AI to predict repair needs and create digital twins of infrastructure elements. Partnering with civil engineering firms to enhance data quality for municipalities, 𝗖𝘆𝘃𝗹.𝗮𝗶 is setting a new standard for how infrastructure is maintained, aiming for a future where every city benefits from smarter, more sustainable transportation systems. Stay tuned with us for the latest updates. 👉 For more information, visit this link: https://tcrn.ch/4aaWydE #innovation #infrastructure #smartcities #technews #trendingnews #techupdates

💦 Check the Article: "Visualization of #Lidar-Based 3D Droplet Distribution Detection for Air-Assisted Spraying" Authors: Zhichong Wang, Zhichong Wang, Yang Zhang, Tian Li, Joachim Müller and Xiongkui He from China Agricultural University and University of Hohenheim 👉Read it in #OpenAccess: https://lnkd.in/dwP8P82R 📜 Abstract: Air-assisted spraying is a commonly used spraying method for orchard plant protection operations. However, its spraying parameters have complex effects on droplet distribution. The lack of large-scale 3D droplet density distribution measurement methods of equipment has limited the optimization of spraying parameters. Therefore, there is a need to develop a method that can quickly obtain 3D droplet distribution. In this study, a 2D LiDAR was used to quickly scan moving droplets in the air, and a test method that can obtain the visualization of 3D droplet distribution was constructed by using the traveling mode of the machine perpendicular to the scanning plane. The 3D droplet distribution at different positions of the nozzle installed in the air-assisted system was tested at different fan rotation speeds, and the methods for signal processing, point cloud noise reduction, and point cloud division for 2D LiDAR were developed. The results showed that the LiDAR-based method for detecting 3D droplet distribution is feasible, fast, and environmentally friendly. 👉 Read it in #OpenAccess: https://lnkd.in/dwP8P82R

Tim and Brent from #nvidia 's Public Sector team has done some of the most amazing work I've seen in #geospatial. This technique below lets you scan in your environment using live drone footage to create a live digital twin of your environment. We have used this to identify objects of interest like wildfires. Because they have scanned it into #omniverse a shared metaverse we can have multiple people/agent all work with the live model to respond.   #gtc24 | #nvidia | #geoint | #mapping | #dod | #dronelife | #commandandcontrol | #jadc2

Thrilled to anounce that our paper: “An Empirical Study of the Generalization Ability of Lidar 3D Object Detectors to Unseen Domains” has been accepted in CVPR 2024 which will take place in Seattle #cvpr24. 3D Object Detection is crucial for the understanding of the 3D environment in automous driving and robotics and LiDAR sensors have greatly boosted the accuracy on this task. However, these detectors suffer greatly when they are deployed in domains they haven’t seen during training (weather conditions, new locations or sensors…). While domain adaptation works seek to transfer the knowledge learned from a source domain to a target domain, they often treat the object detector as a black box, neglecting many architectural decisions and source domain training strategies. Instead, we go back to basics and systematically investigate four design choices in LiDAR based object detection pipelines prior to domain adaptation and the interplay between them: architecture, voxel encoding, data augmentations and anchor strategy. We study the robustness of 9 state-of-the-art models on 6 major benchmarks encompassing 3 main types of domain gaps: location, weather, sensor. We show that simple and careful design choices can perform similar to or even better than domain adaptation techniques all while saving training time. Our main findings: ✅ Transformer backbones with local point features are on average the most robust. ✅ Changing anchor size at test time can notably enhance performance across different geographical locations, indicating that contrary to common beliefs the models have indeed learned a generalizable representation albeit at different scales. ✅Source-domain augmentations allow the model to generalize to low-resolution sensors. ✅Surprisingly, robustness to bad weather is improved when training directly on more clean weather data than on training with bad weather data. We hope these findings can provide practical guidance on developing more robust 3D object detectors. w/: Chongzhe Zhang , Abhishek Kaushik , Karim Guirguis , Mohamed Sayed, and Bin Yang. Thank you for the fruitful cooperation! Paper: https://lnkd.in/dPxNr792 #cvpr2024 #objectdetection #lidar #computervision #deeplearning

If you are working on LiDAR 3D Object Detection, check our latest #cvpr24 paper: „An Empirical Study of the Generalization Ability of LiDAR 3D Object Detection“.

#Laser scanning technology is moving fast! I've recently begun testing the brand new H300 from GreenValley International, and I'm really impressed by how versatile this unit is.💪 Check out some results from its handheld #SLAM capabilities. It can also perform #GNSS integrated scanning (PPK / RTK) when mounted on a vehicle, drone, or the sweet backpack with integrated GNSS they've created for walking in and out of GPS-denied environments. ⚡It features the Hesai XT-32 laser, which generates dense clouds with great range. I'm regularly seeing features ~300ft out in bright outdoor environments. 📱The iOS app allows you to connect and watch the point cloud you're generating in real-time, enabling me to fill in gaps before I finish. ✅A co-aligned Insta360 1-inch camera provides valuable RGB data that LiDAR in this class typically misses and allows for point cloud colorization. It also gives you the freedom to measure from the pano image and project those annotations directly into the point cloud coordinate frame. 💡It has a unique way of tagging control points to geo-reference your scans. On the base of the handheld unit, there's a pointer. Just place the tip on the control point, let it sit for a second, mark it in the app, and add the coordinates later... Very clever! 💻The LiDAR 360 software suite that comes with it is also one of the most comprehensive and powerful pieces of desktop processing software I've used. It's very user-friendly, boasts incredible AI segmentation and vectorization features I'll be demonstrating in future videos, and my favorite feature... no subscriptions. 👏My testing of its SLAM-only capabilities thus far has shown it handling sloppy loop closure and the merging of several individual scans very well. I've had SLAM sessions of 30 minutes without any alignment problems. Point cloud "puffiness" ranges between 1-2cm, which is solid given SLAM. What is SLAM (Simultaneous Localization and Mapping)? Think of SLAM as a navigator and cartographer working in tandem. As the device moves through an environment, SLAM constantly refines its understanding of its own location ("Localization") within that space. Simultaneously, it constructs or updates a digital map of the surrounding area ("Mapping"). It does this by analyzing consecutive 3D snapshots or frames of the environment, aligning them based on recognizable features, similar to photogrammetry.😁 Additionally, SLAM utilizes data from an Inertial Measurement Unit (IMU) to deduce how the device is moving. This process negates the strict need for external references like GPS, allowing for accurate scanning in various settings. 🌍🛰 Overall, I'm truly impressed not just by the hardware but by the full integration of hardware, software, and user experience GreenValley has created here. I'm looking forward to sharing more tests. I was temporarily loaned this unit for independent testing and am not being compensated in any way. Opinions are my own.🤔🧠

If you're familiar with #FiberSensing #DAS and #DVS systems, you should know that your choice of laser can make all the difference! ⚠️ An ultra-narrow linewidth laser #UNLlaser is crucial in fiber sensing applications for several reasons. Let's break down the key aspects: ☑️ Ultra-Narrow Linewidth (1~3 kHz): Ensures a tightly concentrated frequency range, critical for high-precision applications. ☑️ FBG (Fiber Bragg Grating) Technology: Combined with FBG, guarantees single frequency operation for stable and precise output. ☑️ CE Certified under IEC Standards: Meets international safety and performance standards for reliability in applications like DVS/DAS. ☑️ Performance Devices in DVS/DAS Systems: Core component in systems with Raman Laser, APD, AOM, Pulse EDFA, Circulator, WDM, RFA, PIN EDFA for accurate sensing. ☑️ Higher Resolution of the Signal: Ultra-narrow linewidth provides higher signal resolution, crucial for LiDAR, BOTDR, φOTDR, hydrophone, and non-linear research. ☑️ Temperature Control and Anti-Vibration Design: Unique design minimizes the impact of external factors, ensuring stability in output. ☑️ Excellent Performance Characteristics: KHz-level linewidth, low frequency and intensity noise, and SMSR > 50 dB contribute to reliability in demanding sensing applications. Ready to revolutionise your fiber sensing capabilities? Explore the future of precision and get to know Ensure's Ultra-Narrow Linewidth Lasers! 🌐✨ #FiberSensing #UltraPrecision #Innovation

Metasurfaces are artificial materials adept at manipulating perception. With metasurfaces allowing lenses to be reduced to one 10,000th the size of conventional lenses, they are generating considerable interest as optical components allowing miniaturization of optical systems for the next generation of virtual and augmented reality as well as LiDAR. #quantumcomputing #artificialintelligence #quantum https://lnkd.in/dJt9s3_S