标签：#ics

3D Object Detection for Physical AI Applications A robot needs more than image classification. It needs to know: What object is present? Where is it? How large is it? How is it oriented? 3D object detection answers these questions in physical space. 3D Detection Pipeline Camera / LiDAR | v Preprocessing | v Feature Extraction | v 3D Detection Model | v 3D Bounding Boxes | v Tracking / Planning A 3D bounding box can contain: (x, y, z) (width, height, depth) (rotation) (class) (confidence) LiDAR-Based Detection LiDAR naturally provides 3D geometry. A typical pipeline is: Point Cloud | v Filtering | v Voxelization / Features | v Neural Network | v 3D Boxes Challenges include sparse points and computational cost. Camera-Based Detection A camera provides dense visual information. Monocular 3D detection tries to infer depth from a single image, while stereo systems can estimate depth geometrically. Multi-Modal Detection Combining cameras and LiDAR can provide both semantics and geometry: Camera ---> Visual Features --+ | LiDAR ----> 3D Features ------+--> Fusion --> 3D Detection This is useful for autonomous robots operating around people, vehicles, and objects. Post-Processing Raw detections are often filtered using: Confidence thresholds Non-maximum suppression Geometric constraints Temporal tracking Tracking can stabilize detections across frames. ROS 2 Integration A practical architecture: /sensors/camera /sensors/lidar | v /3d_detector | v /objects_3d | +--> /tracker | +--> /planner Use standardized message structures where practical so perception remains decoupled from planning. Measuring Performance Evaluate: Precision Recall 3D IoU Position error Orientation error Inference latency FPS For physical AI, latency matters almost as much as accuracy. A detector that is accurate but too slow can still be unsuitable for a moving robot. Production Considerations Test across: Day/night conditions Different sensor placements Partial occlusion Different object sizes Dynamic

Building a Real-Time SLAM System for Mobile Robots SLAM means Simultaneous Localization and Mapping . A mobile robot must answer two questions: Where am I? What does the environment look like? The challenge is that the robot needs the map to localize while also needing localization to build the map. SLAM Architecture Sensors | +--> Frontend | | | +--> Odometry | +------------------+ v State Estimator | v Map Builder | v Map Sensor Options Typical systems use: 2D LiDAR 3D LiDAR Cameras IMUs Wheel encoders The right sensor combination depends on the environment. SLAM Frontend The frontend extracts motion constraints. For LiDAR: Scan | v Feature / Point Processing | v Scan Matching | v Relative Motion For visual SLAM: Image | v Feature Extraction | v Feature Matching | v Relative Pose Backend Optimization The backend can represent the robot trajectory as a graph: Pose 1 ---- Pose 2 ---- Pose 3 ---- Pose 4 \ / +------ Loop Closure ---+ Loop closure recognizes that the robot has returned to a previously observed location. This can significantly reduce accumulated drift. Real-Time Constraints SLAM is not useful if it produces excellent maps several seconds too late. Monitor: Sensor processing latency Pose estimation latency Map update time CPU/GPU utilization Queue sizes Frame/scan drops Map Resolution Higher resolution gives more detail but costs more memory and computation. Choose resolution based on: Robot size Environment Navigation requirements Available compute Failure Modes SLAM can struggle with: Repetitive environments Dynamic objects Feature-poor walls Rapid motion Poor sensor calibration Incorrect timestamps A robust system should monitor confidence and detect tracking failures. Production Pipeline Camera / LiDAR / IMU | v Sensor Calibration | v Odometry Frontend | v Pose Estimation | v Loop Detection | v Graph Optimization | v Map Server | v Navigation The goal of production SLAM is not just map quality. It is stable localization, predictable latency, and grac

Visual-Inertial Odometry for Autonomous Robots A robot needs to estimate how it moves through the world. GPS is unavailable indoors, wheel odometry can slip, and LiDAR may not always be available. Visual-Inertial Odometry (VIO) combines cameras and IMUs to estimate motion. Basic Idea Camera ---> Visual Features ---+ | v State Estimator ^ | IMU ----> Motion Information ---+ | v Robot Trajectory The camera provides visual constraints. The IMU provides high-frequency motion measurements. Why Combine Them? A camera gives rich spatial information but can suffer from: Motion blur Low texture Poor lighting Slow frame rate An IMU operates at much higher rates but accumulates drift when integrated over time. Their weaknesses are complementary. Feature Tracking A simple visual pipeline might be: Image | v Feature Detection | v Feature Tracking | v Motion Estimation Feature types may include corners or learned visual features. IMU Prediction The IMU can predict how the robot's state changes between camera frames. Conceptually: Previous State | +--> IMU measurements | v Predicted State | +--> Camera observation | v Corrected State This is the prediction/correction pattern used by many estimators. Initialization VIO initialization is important because the estimator must determine quantities such as: Initial orientation Gravity direction Velocity Scale for monocular systems Sensor biases Poor initialization can cause instability later. ROS 2 Architecture /camera/image | v Visual Frontend ----+ | /imu/data ----------+--> VIO Estimator --> /odometry | +--> /tf Use consistent timestamps and calibrated camera-IMU extrinsics. Improving Robustness Useful techniques include: Rejecting outlier feature matches Monitoring IMU saturation Handling dropped frames Estimating sensor biases Detecting low-texture scenes Monitoring estimator health Evaluation Evaluate against a trusted trajectory where available. Useful metrics include: Absolute trajectory error Relative pose error Drift per dista

ROS 2 QoS Profiles: Reliable vs Best-Effort Robot Communication Robot systems continuously exchange data with very different requirements. A dropped camera frame is usually acceptable. A dropped emergency command may not be. ROS 2 Quality of Service (QoS) lets you express these requirements. The Two Common Reliability Modes Reliable Reliable communication attempts to ensure that samples reach compatible subscribers. Useful for: Commands Configuration Important state transitions Critical application data Best Effort Best effort prioritizes timely delivery and may tolerate lost samples. Useful for: Cameras LiDAR High-frequency IMU streams Other continuously refreshed sensor data Example Imagine a camera producing 30 frames per second. If frame 100 is lost, the system can often process frame 101 immediately. For a command: MOVE_FORWARD losing the message may be unacceptable. Therefore: Camera -> Best Effort Command -> Reliable is often a sensible starting point. QoS Dimensions Reliability is only one QoS policy. Important policies include: Reliability Durability History Depth Deadline Lifespan Liveliness C++ Example auto sensor_qos = rclcpp :: SensorDataQoS (); auto publisher = create_publisher < sensor_msgs :: msg :: Image > ( "/camera/image" , sensor_qos ); For important application data, you might explicitly configure reliable communication: auto qos = rclcpp :: QoS ( rclcpp :: KeepLast ( 10 )) . reliable (); auto publisher = create_publisher < std_msgs :: msg :: String > ( "/robot/status" , qos ); QoS Compatibility A publisher and subscriber need compatible QoS settings. A common mistake is: Publisher: Best Effort Subscriber: Reliable and then wondering why messages are not received as expected. Always inspect the effective QoS of both endpoints. A Practical Decision Table Topic Suggested Starting Point Camera image Best Effort Point cloud Best Effort IMU Best Effort Navigation command Reliable Configuration Reliable Robot state Reliable Diagnostics Reliable These

Building a High-Performance Robot Communication System with DDS Modern robots may have dozens of processes distributed across CPUs, edge computers, and embedded devices. ROS 2 uses DDS (Data Distribution Service) as its underlying communication technology. Understanding DDS helps you design robot systems that remain responsive as message traffic grows. The Communication Model Instead of connecting every process directly: Camera ---> Perception LiDAR ---> Perception IMU ---> Localization | v Planning | v Control ROS 2 nodes communicate through DDS topics and discovery. A simplified model is: Publisher | v DDS DataWriter | v Topic | v DDS DataReader | v Subscriber Why DDS Is Useful for Robotics DDS provides mechanisms for: Discovery Reliability Durability Deadline management History Resource limits Data delivery policies These features are important because different robot data has different requirements. A camera stream may prioritize low latency. A configuration message may prioritize reliability. High-Performance Design Avoid treating every topic identically. For example: Data Typical Priority Camera frames Low latency LiDAR scans High throughput IMU Low latency Robot commands Reliability Configuration Reliability + durability Diagnostics Reliability Reduce Copying Large sensor messages can consume substantial CPU and memory bandwidth. Good practices include: Avoid unnecessary serialization/deserialization. Reuse buffers where possible. Keep image resolution appropriate for the workload. Compress only when bandwidth savings justify CPU cost. Separate high-rate sensor topics from low-rate metadata. Separate Data Paths A useful architecture is: +--> Vision Camera ----------+ | LiDAR -----------+--> Perception --> Planning --> Control | IMU -------------+ Diagnostics ---------------------> Monitoring Configuration ------------------> Lifecycle Manager Not all traffic needs the same QoS or processing path. Measuring Performance Do not optimize based on intuition alone.

Today, I’m talking with New York Governor Kathy Hochul, and I’ll just warn you — this episode moves really fast. It’s an election year, after all, with a shocking amount of tech policy at stake, and Governor Hochul has taken strong positions on almost every major tech issue there is. For example, Meta just reached […]

The U.S. is shutting out more foreign-made drones and robots. China’s scale means the global competition may simply move elsewhere.

As backlash grows over Flock's AI surveillance cameras, Texas Governor Greg Abbott has frozen state spending on them. The move came just ahead of the publication of a Texas Tribune investigation that revealed the state spent over $30 million on Flock cameras. That money was primarily raised by tacking a $1 fee onto insurance policies, […]

The discovery redefines how large atoms need to be to produce the extreme state of matter found in the early universe.

1. Introduction Excel is much more than a spreadsheet for entering numbers. It can be used as a data-analysis tool that helps analysts inspect, validate, filter, summarize, and prepare raw data before deeper analysis begins. In typical analytics, the quality of the final work depends heavily on the quality of the data used; therefore, data cleaning is not an optional step—it is the foundation of effective data analysis. This article demonstrates key Week 1 Excel concepts _using an employee dataset containing _employee IDs, names, departments, gender, marital status, hire dates, salaries, educational level, performance score among others. The raw file intentionally contains common data-quality issues: inconsistent capitalization on the First and Last names, blank records, duplicate employee records, varying department names, currency and dates that need review. By working through these issues, the article shows how Excel’s formatting tools, text functions, filters, conditional formatting, numerical functions, conditional summaries, and date functions can turn a messy workbook into an analysis-ready dataset. 2. Why Data Cleaning Matters Data cleaning is more than just about removing errors. By standardizing formats and categories, we make datasets more transparent, usable, and valuable for management analysis and reporting purposes. Data analysis is simple – garbage in, garbage out. A dashboard or prediction can appear professional, but can be misleading if the underlying data has duplicates, blank values, inconsistent categories or incorrectly formatted text and dates. For example, “IT” “I.T.” and “Information Tech” can be viewed as different department values if naming is not standardized. Duplication of an employee ID can inflate employee counts and department totals. A blank performance score might mean that something is missing and should be looked into and dates saved as text cannot be reliably used in calculations such as employee tenure checks. A good practice

Alt-right troll Milo Yiannopoulos was arrested by ICE on Thursday, and today the Department of Homeland Security confirmed to Reuters and the Washington Post that he had been deported to the UK. Yiannopoulos came to the US legally in 2019, but overstayed his visa. After he failed to appear in court for an immigration hearing, […]

I just wrote about the billion-dollar rounds flooding into humanoid robotics. Here is the story from the other end of the scale, and I find it more encouraging. Hugging Face's open-source robot, a 25-centimeter bipedal machine with fifteen actuators and a sensor kit that includes a camera, speaker, LiDAR, NFC, Bluetooth, and WiFi, just passed a million dollars in sales. Fully open hardware, openly documented, quietly making real money. One of these robotics stories is funded like an industrial giant. The other is a small, open, shippable thing that people are actually buying. They are both true, and the small one is the one most builders can learn from. Open hardware turned out to be a business The reflexive assumption about open-source hardware is that you cannot make money on it, because anyone can copy the design. Hugging Face's robot is a live counterexample. The plans are open, the software stack is open through their LeRobot ecosystem, and it crossed a million in sales anyway. That is worth sitting with, because it means openness and revenue are not the opposites people assume. The reason it works is the same reason open-source software companies work. Most buyers do not want to source fifteen actuators, fabricate a chassis, and debug a sensor stack to save money on a robot that already exists and is affordable. They want the finished thing, they want it to work out of the box, and they are happy to pay the people who designed it. Openness is not the giveaway that kills the business. It is the trust and the ecosystem that make the business, because you can see exactly what you are buying, modify it, and build on a platform other people are also building on. Why this is the better story for builders The mega-funded humanoid companies are placing a bet only a handful of players can place: billions of dollars, years of runway, factories. That is a real path, and it is not your path or mine. The Hugging Face robot is the other path, and it is copyable. Small, open

Technical progress has encouraged a new batch of companies to jump in on the promise of profits from humanoid robots. And they're all Chinese automakers.

The longtime far-right operator and troll, a UK citizen, is being held in ICE custody pending his removal from the United States.

“Do we need to look at the ceiling before going to the bathroom? Can we actually trust the water is safe to drink?” asks one GSA worker.

This essay was written with Kasra Rafi, and originally appeared in The Guardian. Earlier this month, about 40 top mathematicians gathered at OpenAI’s offices to discuss the future of their profession. The meeting was off-the-record, but if recent articles by mathematicians are any guide, it was mostly pretty glum. People fear for their jobs, their careers and the work they love. We think the contrary view is more likely, at least in the short-term. AI models are nowhere near as capable as experienced academic mathematicians. This isn’t to say that AIs aren’t producing stunning mathematical results at the level of PhD researchers. In mid-May, OpenAI ...

Influential right-wing conspiracy theorists and MAGA-aligned pundits are falsely claiming that the Covid vaccine killed Dolly Parton, the vaccination advocate and philanthropist.

A federal judge has called the Department of Defense’s designation of Anthropic as a national security supply-chain risk “illegal and baseless.”

Hugging Face is taking orders for the Microduck, a $399 tiny open-source duck robot that developers can train at home out of the box.

If you've ever wanted a robotic duck as a companion, now's your chance.