AI systems demand increasing amounts of data to perform well—but gathering real-world data can be costly, slow, and limited in scope. That’s why many teams are turning to synthetic data: it’s faster to generate, easier to customize, and ideal for covering rare or hard-to-capture scenarios.
This blog shares our first hands-on experience with Tier IV’s Edge.Auto perception framework. After validating the perception modules in the CARLA simulator, we took the next step by deploying the same pipeline on the AVerMedia D135 embedded platform. This journey represents a practical attempt to bridge the gap between simulation and real-world deployment, helping us better understand how to run ROS 2-based perception logic on edge hardware.
This tutorial walks you through setting up an Autonomous Mobile Robot (AMR) using Jetson with the AVerMedia D115W platform and NVIDIA Isaac ROS. We cover sensor integration, depth estimation (ESS), Visual SLAM, costmap generation, and autonomous navigation with Nav2.
Imagine walking up to a kiosk in a busy fast-food restaurant. Instead of tapping through a complex menu on a touchscreen, you simply say, "I'd like a cheeseburger, no onions, with a side of fries and a vanilla milkshake." The kiosk confirms your order instantly, and you're ready to pay. This isn't science fiction; it's the future of customer interaction, and it's powered by AI running directly at the edge.
Time to First Token (TTFT) refers to the latency between a user hit the Enter key and the appearance of the first character shows on the screen. Excessive TTFT can greatly diminish the overall user experience.
TTFT is a crucial response time indicator for an online interactive application powered by a large language model (LLM), as it reflects how quickly users can catch the first character from the model through a web page.
Here, we will explore two simple ways to get the latency of first token from a language model.
This article explains how to set up and install NVIDIA Isaac ROS on Jetson platforms, including Docker configuration, SSD integration, developer environment setup, and compatibility between different JetPack and Isaac ROS versions.
In this article, we explore how artificial intelligence (AI) can dramatically improve the speed and accuracy of retail checkout processes, helping businesses reduce training costs and improve customer satisfaction.
Deploying SLAM on embedded platforms remains a challenge. In this article, we evaluate whether the Qualcomm® QCS6490 platform can reliably support Depth VSLAM for AMR and smart vision applications.
By leveraging ROS 2 VSLAM nodes provided in the QIRP SDK, we aim to measure system feasibility, performance, and identify optimization points for real-world deployment.
QL601 is a powerful single-board computer equipped with Qualcomm® QCS6490 chipset, along with AVerMedia software stack, helping developers to build AI-powered multimedia applications.
In this tutorial, we will guide you through the steps to set up the QL601 development environment, helping you to get started with the QL601 quickly.
Qualcomm AI Hub provides various AI models optimized for Qualcomm devices. This guide introduces two methods for downloading these models:
qai-hub-models.You'll learn how to access all the Qualcomm-provided models, including those with licensing restrictions like YOLOv8 and YOLOv11.