Wireless indoor localization has attracted significant research interest due to its high accuracy, low cost, lightweight design, and low power consumption. Specifically, ultra-wideband (UWB) time difference of arrival (TDOA)-based localization has emerged as a scalable positioning solution for mobile robots, consumer electronics, and wearable devices, featuring good accuracy and reliability. While UWB TDOA-based localization systems rely on the deployment of UWB radio sensors as positioning landmarks, existing works often assume these placements are predetermined or study the sensor placement problem alone without evaluating it in practical scenarios. Through extensive real-world experiments, we demonstrate the accuracy and robustness of our localization system, comparing its performance to the theoretical lower bounds. Using a challenging multi-room environment as a case study, we illustrate the full system construction process, from sensor placement optimization to real-world deployment. Our evaluation, comprising a cumulative total of 39 minutes of real-world experiments involving up to five agents and covering 2608 meters across four distinct scenarios, provides valuable insights and guidelines for constructing UWB TDOA localization systems.

Collecting real-world manipulation trajectory data involving robotic arms is essential for developing general-purpose action policies in robotic manipulation, yet such data remains scarce. Existing methods face limitations such as high costs, labor intensity, hardware dependencies, and complex setup requirements involving SLAM algorithms. In this work, we introduce Fast-UMI, an interface-mediated manipulation system comprising two key components: a handheld device operated by humans for data collection and a robot-mounted device used during policy inference. Our approach employs a decoupled design compatible with a wide range of grippers while maintaining consistent observation perspectives, allowing models trained on handheld-collected data to be directly applied to real robots. By directly obtaining the end-effector pose using existing commercial hardware products, we eliminate the need for complex SLAM deployment and calibration, streamlining data processing. Fast-UMI provides supporting software tools for efficient robot learning data collection and conversion, facilitating rapid, plug-and-play functionality. This system offers an efficient and user-friendly tool for robotic learning data acquisition.

Simultaneous Localization and Mapping (SLAM) is an essential component of autonomous robotic applications and self-driving vehicles, enabling them to understand and operate in their environment. Many SLAM systems have been proposed in the last decade, but they are often complex to adapt to different settings or sensor setups. In this work, we present LiDAR Graph-SLAM (LG-SLAM), a versatile range-inertial SLAM framework that can be adapted to different types of sensors and environments, from underground mines to offices with minimal parameter tuning. Our system integrates range, inertial and GNSS measurements into a graph-based optimization framework. We also use a refined submap management approach and a robust loop closure method that effectively accounts for uncertainty in the identification and validation of putative loop closures, ensuring global consistency and robustness. Enabled by a parallelized architecture and GPU integration, our system achieves pose estimation at LiDAR frame rate, along with online loop closing and graph optimization. We validate our system in diverse environments using public datasets and real-world data, consistently achieving an average error below 20 cm and outperforming other state-of-the-art algorithms.

How will mobile phones evolve? Will they exist in the future? Let us use two AI models…ChatGPT (https://chat.openai.com/) ChatGPT will be used to generate future scenarios/ products & Midjourney will be used to visualize the product designs. Here is the prompt to get design ideas.

A breathalyser that spots Covid-19, a robotic massage table, and an ultraportable electric wheelchair are among the many bizarre inventions unveiled at CES 2022. The event is being held in person in Las Vegas, Nevada, as well as online for people who can't travel - with tens of thousands of ideas, concepts and products on show. The massage table, by Massage Robotics, has two arms and responds to verbal commands in real time, but it has a whopping $310,000 (£228,000) price tag, and was just one of a number of relaxation devices on show at CES this year. Covid-19 is present throughout the event, including in the absence of some major companies such as Amazon and Google, but it is also present in the purpose of a number of products on display, including a breath analyser that detects the virus. The annual event runs until Saturday, and MailOnline has created this roundup of some of the weird and wonderful inventions revealed by firms large and small.

Christmas is just around the corner, which means it's time to start planning your presents. Finding the perfect gift for your loved ones can be tricky, but don't worry, TechRadar is here to help you plan ahead. There's nothing like watching the people you care about erupt into smiles as they tear off your wrapping, and are greeted with a gift they actually love. So if you want to leave a lasting impression, the latest tech gadget can do just that. Technology is evolving so quickly that if you decided on a gizmo last year, there's always something new to choose from this year.

Today, any typical modern-day smartphone is able to scan faces, documents, QR codes, capture super-resolution photos, recognise gestures, voice and perform multiple other tasks besides answering calls and texts. These handheld devices are the epitome of software and hardware engineering; and to do these tasks, they require state-of-the-art image recognition and NLP models running in the background. Image and language models are at the heart of many machine learning applications today and training these models is a computational nightmare with increasing data. Google has been using TensorFlow Lite for taking pictures on its flagship model Pixel. For Portrait mode on Pixel 3, Tensorflow Lite GPU inference accelerates the foreground-background segmentation model by over 4x and the new depth estimation model by over 10x vs CPU inference with floating-point precision.

Recent introduction of wearable single-lead ECG devices of diverse configurations has caught the intrigue of the medical community. While these devices provide a highly affordable support tool for the caregivers for continuous monitoring and to detect acute conditions, such as arrhythmia, their utility for cardiac diagnostics remains limited. This is because clinical diagnosis of many cardiac pathologies is rooted in gleaning patterns from synchronous 12-lead ECG. If synchronous 12-lead signals of clinical quality can be synthesized from these single-lead devices, it can transform cardiac care by substantially reducing the costs and enhancing access to cardiac diagnostics. However, prior attempts to synthesize synchronous 12-lead ECG have not been successful. Vectorcardiography (VCG) analysis suggests that cardiac axis synthesized from earlier attempts deviates significantly from that estimated from 12-lead and/or Frank lead measurements. This work is perhaps the first successful attempt to synthesize clinically equivalent synchronous 12-lead ECG from single-lead ECG. Our method employs a random forest machine learning model that uses a subject's historical 12-lead recordings to estimate the morphology including the actual timing of various ECG events (relative to the measured single-lead ECG) for all 11 missing leads of the subject. Our method was validated on two benchmark datasets as well as paper ECG and AliveCor-Kardia data obtained from the Heart, Artery, and Vein Center of Fresno, California. Results suggest that this approach can synthesize synchronous ECG with accuracies (R2) exceeding 90%. Accurate synthesis of 12-lead ECG from a single-lead device can ultimately enable its wider application and improved point-of-care (POC) diagnostics.

The "smart display" wars are taking off, which means it's time for consumers to ask themselves that modern existential question: Do I need another screen? That's what I was wondering while sitting in the bedroom of a Google employee, whose chic San Francisco home was being used as the backdrop for demos of the company's new Home Hub device. Major tech companies, one after the other, have launched voice-controlled touchscreen gadgets that are meant to live on your kitchen counter, nightstand or living room table. And Google Homes product manager Ashton Udall was showcasing what this particular one does when you wake up and say, "Okay, Google. At this prompt, Google's virtual assistant voiced a greeting, announced the time and weather, then provided an assessment of how bad the commute would be that day. Meanwhile, the bedroom shade -- one of the 10,000 or so smart devices that can sync with the Home Hub -- automatically rolled up. When the assistant is done going through reminders or previewing events on the Google Calendar, it might launch a news reel. This one had been programmed to segue into classical music instead. "In the morning, you're stumbling out of bed, you're getting the cobwebs out of your head," Udall said. "I don't have to go into my phone … You can start just listening." Smart displays, though their capabilities vary, are not replacements for handheld devices. They're meant to be shared. The Home Hub is in many ways a smart speaker with a 7-in. Yet supplanting those handheld devices is a value proposition that Google employees emphasized when I asked why people need this, as if the smart display would function as a Plexiglass partition between me and my smartphone -- servicing many of my basic needs and desires without exposing me to distracting, endless notifications. "The way we designed this is it's there.

A team of researchers at the Massachusetts Institute of Technology is working to bring neural networks to handheld devices. Neural networks comprise thousands or millions of simple and densely interconnected nodes that are organized by layers. Each interconnection has a weight and those weights are continually readjusted as the network is changed. All that computation requires a lot of memory and power, so neural networks are better suited to run on servers than smartphones. But last year a team led by Vivienne Sze, an associate professor of electrical engineering and computer science at MIT, developed an energy-efficient chip designed for neural networks that might allow them to run on smartphones.