Lukas Timm’s Post

How does an interactive LiDAR convert the received signal into the coordinates of a touch point? The secret lies in LiDAR! LiDAR stands for Light Detection and Ranging. It works by emitting laser pulses and measuring the time it takes for the light to bounce back. By calculating the time of flight, LiDAR can accurately determine the distance to an object, like your finger. It's like using a laser tape measure, but on a much smaller scale and at incredibly high speeds. Imagine a bat using echolocation to navigate in the dark. LiDAR does something similar, but with light. Instead of sound waves, it uses laser pulses to create a detailed 3D map of its surroundings. This is what allows it to detect your finger's precise position in space. Why LiDAR? Precision: LiDAR offers unparalleled accuracy, allowing for incredibly detailed and responsive interactions. Non-contact: Unlike traditional touchscreens, LiDAR doesn't require physical contact, making it more hygienic and suitable for public spaces. 3D capabilities: LiDAR can capture 3D data, enabling more complex and immersive interactions. Applications: Virtual and augmented reality: Create truly immersive experiences. Autonomous vehicles: Enable self-driving cars to perceive their environment. Robotics: Improve robot perception and manipulation. Smart homes: Enable gesture-based controls and advanced home automation. Want to learn more? Check out ： [email protected] whatsapp:+86 186 1615 2997 #LiDAR #robot #robotic #interactivetechnology #userinterface #augmentedreality #virtualreality #technology #engineering #innovation #computerscience #physics

How Hard Can It Be? Making Robotaxis a Reality Turns out, it might be one of the biggest engineering challenges we face today. Harder than space travel (just look at the latest from SpaceX). Harder than autonomous flight. It’s not just about making a car drive itself. It’s about solving the “state machine” problem — a level of reliability that handles ethical concerns, safety, and unpredictability in real-time. It’s 🤯. Let’s dive into the architecture of an autonomous robotaxi system. Buckle up! Anatomy of an Autonomous Robotaxi System 1. Sensing System The robotaxi’s eyes and ears: -> LiDAR: Maps 3D surroundings to detect objects. -> Cameras: Recognizes traffic signs and tracks lanes. -> Radar: Measures speed and distance, critical in tough weather. -> IMU & GNSS: Stabilize and pinpoint the car’s location. 2. Perception This is where data turns into decisions: -> Sensor Fusion: Merges inputs from LiDAR, cameras, radar. -> Object Tracking: Predicts movement of other objects on the road. 3. Localization & Mapping Robotaxis don’t just rely on GPS: -> HD Maps & SLAM: Create and update maps in real time for ultra-precise navigation. 4. Planning How do we get from point A to point B? -> Path & Behavior Planning: Ensures safe navigation through complex city environments while following traffic rules. 5. Control Systems Translating data into action: -> Actuation & Feedback Loops: Steering, braking, and acceleration get constant real-time adjustments. 6. Communication The robotaxi talks too: -> V2X & Remote Monitoring: Enables data sharing with other vehicles and external monitoring systems. 7. Safety Systems Redundancy is everything: -> Backup Systems & Emergency Protocols: Ensure the car can always stop or navigate safely in case of failure. By no means is it easy. In fact, it’s a massive undertaking. But every breakthrough gets us one step closer to a world where robotaxis are the norm. (What do you think? Is it the future or just a pipe dream?) ♻️ If you found this helpful, consider resharing to your network!

Ever wondered how 3D maps or self-driving cars capture every detail with pinpoint accuracy? That’s where LiDAR comes in transforming industries by capturing the world in stunning detail. LiDAR, or Light Detection and Ranging, is a groundbreaking technology that works by firing laser beams at objects and measuring how long they take to bounce back. These measurements are then used to create accurate 3D representations of the environment. It's like giving machines a superpower to perceive the world with precision. But what makes LiDAR so special, and why are industries racing to adopt it? Key Features of LiDAR: ➡️ High Precision: Captures minute details with centimeter-level accuracy. ➡️ Wide Range: Scans large areas quickly and efficiently. ➡️ Works in Any Light: Operates day or night, even in low-light conditions. ➡️ Dense Point Clouds: Produces rich, detailed datasets ideal for mapping and modeling. Because it can provide detailed insights, LiDAR has found applications in some of the most advanced technologies and industries. Its applications include creating real-time 3D maps for autonomous vehicles, enabling precise urban planning, optimizing 5G networks with detailed obstacle detection, and more. Why LiDAR Data Needs Annotation? The power of LiDAR lies in its ability to generate dense, complex datasets. However, to turn these raw point clouds into actionable insights, data annotation is the key. That’s where platforms like Taskmonk step in, helping annotate LiDAR data with precision for: 🔹 Obstacle Detection: Labeling objects like poles, vehicles, and buildings. 🔹 Terrain Analysis: Identifying elevation changes and surface types. 🔹 Precision Applications: Ensuring high-quality data for AI-driven solutions. LiDAR captures the world and also understands it. With annotated LiDAR data, industries are breaking new ground in innovation, creating smarter cities, safer roads, and more efficient systems. Want to learn more about how LiDAR is shaping the future? Follow us! #taskmonk #lidar #dataannotation #aiinnovation

🔲 WILL RADAR SOON REPLACE LiDAR? 4D IMAGING RADAR BASED ON TDA4. 👉 When moving from 3D to 4D RADARs, we got a much better resolution, and less noise. What we can note is that LiDAR and Radar sensors got better, and could work as standalone? The vid compares a 4D Radar with the new 4D Radar/TDA4. ◻ LiDAR: The FMCW makes it better with weather conditions and adds the velocity estimation. ◻ RADAR: The overall better resolution removes lots of noise, and makes it easier to find distances, classify, etc... 👉 Altos V2 is probably, the world's first 4D imaging radar product that's based on the TI's TDA4 processor. It is designed for ADAS and full autonomous driving application. 👉 The TDA4VM provides high performance compute for both traditional and deep learning algorithms at industry leading power/ performance ratios with a high level of system integration to enable scalability and lower costs for advanced automotive platforms supporting multiple sensor modalities in centralized ECUs or stand-alone sensors. 👉 Yellow - Lidar points, serving as ground truth in both cases. 👉 Green - stationary points from Radar 👉 Red - approaching points from Radar 👉 Pink - departing points from Radar 👉 The grid is 20m x 20m 👉 Identical setting for both cases left/right ◻ The test location is Beijing Road in Beijing, China. ◻ Founded in January 2023 by experts of leading tech firms including Apple, Pony.ai and Mozilla, Altos Radar secured investments from venture capitalists - ZhenFund 真格基金, Monad Ventures and Hesai CEO, Yifan Li, ◻ In an outstanding company move, former head of next-generation Imaging Radar technology at Bosch (Germany), Dr. Mingkang Li, has been appointed President at Altos Radar, in 2024. video: Altos Radar/globalnetwork #Altos #AltosV2 #AltosRadar #LiDAR #4DImaging #TexasInstruments #sensor #autonomousdriving #ADAS #Bosch Li Niu Michael Wu Mingkang Li Altos Radar Texas Instruments LightWare LiDAR Bosch Mobility

Presented Digital S/W Development & Validation for Top Autonomous Mobile Robot for warehouse automation provider. AMRs rely on advanced sensors(Camera, LiDAR & Ultrasonic sensors) , navigation algorithms and AI driven decision making capabilities to operate in dynamic environments. Capabilities it provides in Warehouse , Manufacturing facilities , Hospitals & CG sector automation is path breaking. #AMR #DataAnalytics #AI #FacilityIntegration #Security

Hi folks, Sonair’s Acoustic Sensing Breakthrough in AMRs! 🚀🤖 Sonair’s $6.8M funding is set to shake up object detection tech for autonomous mobile robots with its innovative 3D acoustic detection and ranging (ADAR) system. Here’s what stands out: •🌐 𝗙𝗿𝗼𝗺 𝟮𝗗 𝘁𝗼 𝟯𝗗 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: Sonair’s ADAR uses ultrasonic sensors for 3D coverage, improving precision over traditional LiDAR. •🔊 𝗦𝗮𝗳𝗲, 𝗤𝘂𝗶𝗲𝘁 𝗨𝗹𝘁𝗿𝗮𝘀𝗼𝗻𝗶𝗰𝘀: With an 80 kHz frequency, the system is effective yet unobtrusive in environments like warehouses or healthcare facilities. •⚙️ 𝗖𝗼𝘀𝘁-𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 & 𝗟𝗼𝘄 𝗣𝗼𝘄𝗲𝗿: ADAR cuts down on battery drain and processing power, ideal for AMRs navigating crowded spaces. Could this be the future of AMR sensing? Read the full news below 👇: https://cstu.io/2451ca #AMR #UltrasonicSensors #Automation #Robotics 🌍

In the world of autonomous navigation, sensor fusion is a game-changer. By combining data from multiple sources like LiDAR, cameras, and GPS, sensor fusion provides a more accurate, reliable, and robust understanding of an environment. This synergy enables autonomous systems to “see” the world in ways that individual sensors cannot achieve alone. But there’s a key factor that often gets less attention—choosing the right data structure to manage and integrate this information effectively. Using optimal data structures can significantly enhance the efficiency of sensor fusion algorithms. For instance, data structures that enable quick access, sorting, and updating of sensor inputs can reduce processing time, making real-time decision-making faster and more reliable. Structures like octrees or KD-trees allow for efficient spatial organization of data, especially useful when handling large amounts of 3D point cloud data from LiDAR or managing multi-dimensional sensor data for complex environments. By prioritizing both sensor fusion and smart data structure choices, autonomous systems become not only more effective but also more efficient, paving the way for safer, faster, and more reliable navigation. As the field progresses, the marriage of these technologies will continue to drive innovation, bringing us closer to a future where autonomous systems can seamlessly and safely navigate our world. #AutonomousNavigation #SensorFusion #DataStructures #AutonomousVehicles #Efficiency #Innovation

How Autonomous Robots ‘See’ the World: Sensors and Perception Perception is key to autonomy. Autonomous robots gather information about their surroundings through multiple sensors, including LiDAR, cameras, and ultrasonic sensors. These sensors enable the robot to 'see' and understand its environment, making real-time decisions about navigation, obstacle avoidance, and task execution. Robust perception systems need to handle varying conditions, from lighting changes to occlusions, while maintaining accuracy. Integrating data from diverse sensors ensures more reliable and precise perception, especially in complex and unpredictable environments. #PerceptionInRobotics #SensorFusion #AutonomousSystems

How Robots See the World?! Robots rely on a combination of sensors to "see" the world, and a key player is lidar. This technology uses lasers to create a 3D map of the environment, but it has limitations. While lidar excels at measuring distance, it can miss crucial details. Take low-lying obstacles or uneven terrain – these might be invisible to a standard lidar. NVIDIA with their Isaac Perceptor system are using multiple cameras and AI to give robots a more complete picture. Imagine robots with 3D surround vision, able to not only gauge distance but also understand the objects they see! NVIDIA isn't the only player in the robot vision game. Companies like Boston Dynamics with their advanced cameras and Inovance with their depth sensors are all pushing the boundaries of robot perception. Robots are getting smarter, and their vision is getting better. This means safer collaboration between robots and humans, and opens doors for even more intelligent and versatile robots in the future! Follow Endrit Restelica to stay up to date with AI. #ai #tech #robotics #innovation #gtc2024

🚗🔧 Traditional and basic imaging radars struggle with object classification, often failing to distinguish between different types of objects, especially at long ranges. Advanced imaging radars offer some improvement, but limitations persist. Perception radars change the game by providing a detailed point cloud used as input for AI algorithms, enabling precise classification of cars, pedestrians, bicycles, and even more detailed distinctions like trucks vs. cars or bicycles vs. motorcycles. Additionally, perception radars excel in Free Space Mapping, creating detailed maps of the environment by identifying drivable spaces and obstacle-free areas. This feature is crucial for safe path planning in hands-free and eyes-off driving scenarios. Sensor fusion is another strong point for perception radars. While traditional and basic imaging radars require complex processing for fusion, perception radars generate high-resolution data readily compatible with cameras and LiDAR, enhancing the environmental awareness of self-driving cars. These advancements make perception radars indispensable for the future of automotive. Read more: https://lnkd.in/dwAjwfSU #RadarTechnology #SelfDrivingCars #Innovation #AutomotiveTech #SensorFusion