AI-Data-Pipeline

A futuristic environment with a self driving vehicle and a satellite above a city with skyscrapers. The vehicle is equipped with all kinds of sensors.

A framework for selecting sensor data sources in a fast-moving company

Effective sensor selection isn’t about deep research; it’s about making fast, disciplined decisions that control commitment and protect your company’s strategic freedom.

A street with busy traffic flow, whereas labels are put on top of the image to identify objects

The crucial role of data labeling in machine learning

Data labeling is essential for training AI in autonomous systems, balancing manual and automated methods while addressing privacy, accuracy, and scalability challenges.

Machine-Learning Autonomous-Systems Computer-Vision AI-Data-Pipeline

A big lens installed on top of a self-driving vehicle with a calibration unit installed in front of the vehicle

Navigating the challenges of data capture in mobile robotics

Outlining key challenges in real-world data capture—like occlusion, lighting, dust, and poor calibration.

Machine-Learning Autonomous-Systems Computer-Vision AI-Data-Pipeline Robotics

A number of self driving vehicles and drones on platforms

The technical terrain of capturing data within mobile robotics

This post examines key sensor technologies for automation and their trade-offs, while also addressing cybersecurity, data processing, and the widespread underuse of captured sensor data.

Machine-Learning Autonomous-Systems Computer-Vision AI-Data-Pipeline Robotics

Robot in a warehouse capturing lidar data

Defining data in the age of automating mobility

This first DIVI post defines Data as raw sensor input essential for automation, stressing its quality and fusion as key to reliability.

Machine-Learning Autonomous-Systems Computer-Vision AI-Data-Pipeline Robotics

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