Blogs

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VSLAM Blog 2 featured Photo
The second part of the Visual SLAM blog series discusses one of the key components in visual SLAM: the feature extraction pipeline, which includes triangulation and map point creation. These steps play a critical role in accurately estimating the robot's pose and creating a map of the surrounding environment.
RISC-V Blog featured image
RISC-V is an open-source instruction set architect (ISA) based on reduced instruction set computing principles.
VSLAM featured image
This Series of blogs explores the exciting field of Feature based Visual Simultaneous Localization and Mapping (VSLAM). It also discusses the two state-of-the-art algorithms that are widely used in this area: RTAB-Map and ORB-SLAM3.
Automotive ECUs featured image
One of the biggest challenges faced by OEMs today is automotive cyber security with the amount of hardware and software integrated into vehicles increasing significantly over the last few decades. There can be as many as 100 ECUs embedded in modern vehicles.
Performance Evaluation featured image
Performance Evaluation is important to do comparative study of different algorithms and decide which algorithm is better than others.
In the ever-evolving landscape of programming languages, Python has established itself as a favorite among developers for its simplicity, readability, and extensive libraries. Moreover, we love Python. 
Smart Infrastructure Inspection featured image
Civil infrastructure like roadways, bridges, tunnels, pipelines, transmission towers, residential and commercial buildings require continuous monitoring to ensure structural integrity.
Renesas Pretrained AI Libraries featured image
If you found our previous blog 'Ignitarium Releases Pre-trained AI Applications Library for Renesas RZ/V2L' interesting, we are back with the second release of pretrained AI libraries targeted for Renesas RZV2L device.
Railroads are the connective tissue of our world’s infrastructure. But despite their critical role in global transportation and supply chains, most railroad track maintenance begins with a human inspector.
Featured Image
As design complexity is increasing, the goal of 100% functional coverage becomes harder to achieve even after using constrained random stimulus and directed scenarios, therefore there is a need to adopt new methods of validation.
3D Lidar Loop Closure featured image
Robots are all equipped with simultaneous localization and mapping (SLAM) algorithms that help them build a map of its surroundings. For SLAM to produce reliable results, loop closure detection and correction are essential.
3D Lidar SLAM- Localization featured image
Localization is the process of determining a mobile robot's location in relation to its surroundings. Let's imagine that the area has been mapped out and that the robot has sensors to both monitor its surroundings and gauge its own mobility.
Graph-based SLAM (also known as Graph SLAM) uses a graph to represent the environment and the robot’s pose estimates. It is widely used in many robotics applications like autonomous vehicles, mobile robots and unmanned aerial vehicles.
Featured Image
Simultaneous Localization and Mapping (SLAM) is a popular technique in robotics that involves building a map of an unknown environment while simultaneously localizing the robot within that environment.
Featured Image
Simultaneous Localization and Mapping (SLAM) is a technology used in robotics and autonomous systems to create maps of unknown environments while simultaneously tracking the location of the robot within that environment.
Horizontal scaling featured image
Video is a common data input in the field of Computer Vision & Image processing. For example, in applications such as infrastructure maintenance and defect detection, video is captured from a camera source mounted on a drone or a locomotive.
In the automotive industry, safety plays an immensely crucial role. All crucial systems of the automobile need to pass stringent functional safety requirements.
AI Model optimizing featured photo
In this article we explore the advantages of making use of the native APIs and runtime engine of OpenVINO to maximize the performance and efficiency of DNN model inference.