The rise of autonomous systems, particularly autonomous mobile robots (AMRs), is revolutionizing industries, impacting labor demand and trade deficits. Labor Shortages and Growing Demand From a macro perspective, deploying advanced technology solutions has significant market impact. As we analyze labor and demand, it becomes evident that while the demand for labor is steadily increasing year…
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Introduction: Sensor calibration is essential for accurate data interpretation and fusion in various applications, such as autonomous vehicles, robotics, and remote sensing. This blog post will provide a step-by-step guide on how to calibrate sensors using Fisheye GL and LiDAR projection mapping onto different camera perspectives using HTML5 Canvas and JavaScript. Additionally, we will discuss…
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Image stitching is the process of combining multiple images together to create a seamless panorama or a large-scale image. This technique is commonly used in photography, virtual reality, and mapping applications. In this blog, we will take a deep dive into the concepts of image stitching, including sensor and vehicle frame of reference, origin point,…
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Collecting failure cases from a fleet of L3 ADAS vehicles is a crucial task in ensuring the safety and reliability of these systems. With the increasing use of advanced driver assistance systems (ADAS) in vehicles, it is important to have a reliable and efficient method for collecting and analyzing data from these systems. One way…
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Coordinate frames are an important aspect of robotics and computer graphics, as they determine the position and orientation of objects in 3D space. However, different platforms and software libraries use different conventions for their coordinate frames, which can lead to confusion and errors when working with multiple systems. Three.js, ROS, and Unreal Engine are all…
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ADAS enabled vehicles are most commonly driven on highways and main roads for a number of reasons. One reason is that these types of roads tend to have less complex driving scenarios compared to residential areas or side roads. Highways and main roads often have fewer intersections and more predictable traffic patterns, which can make…
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WebGPU is a new technology that allows developers to take advantage of the power of the GPU (graphics processing unit) in modern browsers. It allows for faster and more efficient processing of complex tasks, including machine learning algorithms. One of the key benefits of WebGPU is its ability to support federated learning. Federated learning is…
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Data backhauling refers to the process of transferring data from one location to another, typically from a remote or geographically dispersed location to a central location or “backhaul.” This process is often used in industries such as transportation, where data from vehicles or other mobile assets is collected and sent back to a central location…
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CVAT (Computer Vision Annotation Tool) is an open source tool developed by Intel that allows users to label and annotate images and video data for training machine learning models. MLFlow is an open source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiment runs, organizing code, and reproducing runs, among…
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CVAT (Computer Vision Annotation Tool) is an open-source annotation tool for computer vision tasks that allows users to label and manage large datasets quickly and efficiently. Integrating CVAT with an MLFlow framework can streamline the data labeling process and make it easier to track and analyze the performance of your machine learning models. Here is…
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Removing dimensions or making invariant features is a technique used to reduce the search space in a problem by eliminating certain variables or making them irrelevant. This can be especially useful in LiDAR slam, which has a 6Dof search space (x, y, z, roll, pitch, yaw). By reducing the dimensions, the number of permutations in…
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Data balancing is the process of ensuring that a machine learning dataset is representative of the real-world population from which it is drawn. This is important because if a dataset is biased, then the machine learning model that is trained on that dataset will also be biased. Bias in machine learning models can lead to…
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Data structures are the foundation of efficient algorithms and play a crucial role in determining the time complexity of a piece of code. Time complexity refers to the amount of time it takes for an algorithm to complete, and it is a measure of how the runtime of an algorithm grows as the input size…
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ROS (Robot Operating System) is a popular open-source robotics framework that provides libraries and tools for building robot applications. There are currently two versions of ROS: ROS 1 and ROS 2. In this article, we will explore the tradeoffs and advantages of both versions and discuss the tools available for converting between them. We will…
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Autoencoders are neural network architectures that are used to learn a compact representation of input data, called the encoding, and then reconstruct the input data from this encoding. Autoencoders can be used to process point cloud data, which is a set of points in space that represent the surface of an object, as well as…
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