Autonomous

Two-Wheeler Driving

Stereovision cameras reconstruct 3D scenes by triangulating corresponding points from two or more viewpoints. This article explains passive and active stereo modalities, epipolar geometry, calibration, disparity estimation, and error sources. It compares stereovision with ToF and structured light, and maps applications across robotics, AR/VR, automotive, industrial metrology, and healthcare, with practical design notes and references for reproducible deployment.

Computer Vision enables machines to interpret and analyze visual data. This article explains fundamental principles, core techniques such as image processing, feature extraction, and deep learning, along with calibration and error sources. It compares computer vision with human vision, and maps applications across robotics, AR/VR, automotive, healthcare, and industry, with practical design notes and references for reproducible deployment.

The Robot Operating System (ROS) is an open-source middleware framework that simplifies robotics software development. This article explains ROS architecture, communication models, tools, and ecosystem. It compares ROS 1 and ROS 2, highlights benefits and limitations, and maps applications across robotics, automation, healthcare, automotive, and industry, with practical design notes and references for reproducible deployment.

Point clouds are collections of 3D data points representing surfaces and environments. This article explains acquisition methods, processing challenges, and algorithms for segmentation, classification, and reconstruction. It compares point clouds with other 3D data formats and maps applications across robotics, autonomous driving, AR/VR, healthcare, and industry, with practical design notes and references for reproducible deployment.

Global Navigation Satellite Systems (GNSS) provide worldwide positioning, while Real-Time Kinematic (RTK) enhances accuracy to centimeter levels. This article explains GNSS fundamentals, RTK working principles, error corrections, and compares them with DGPS. It maps applications across surveying, agriculture, robotics, UAVs, and autonomous navigation, with design considerations and references for reproducible deployment.

Computer vision enables autonomous vehicles to perceive and interpret their surroundings. This article explains core principles, algorithms for detection and tracking, sensor fusion, and challenges such as weather and occlusion. It compares computer vision with LiDAR and radar, and maps applications across navigation, safety, and industry, with design considerations and references for reproducible deployment.

Hardware-software integration bridges embedded systems and applications. This article explains integration using microcontrollers (MCUs), development boards, and FPGAs. It covers workflows, toolchains, and design considerations, compares architectures, and maps applications across robotics, IoT, automotive, and industry. Practical notes highlight debugging, scalability, and reproducibility, with references for engineers seeking robust, real-world deployment.

Autonomous vehicles rely on multimodal sensors, data fusion, and machine learning for perception and decision making. This article elaborates the complete chain: sensor acquisition, preprocessing, fusion, prediction, and control. It emphasizes machine learning techniques for trajectory prediction, risk assessment, and decision making, mapping applications across navigation, safety, and industry, with design considerations and references for reproducible deployment.