Cracking the code for robot fleets in harsh environments
Oil and gas sites are adding more ground robots and drones, but managing them all under one system isn’t easy.
Autonomous-Systems
Bridging the gap between Data and Impact in Autonomous Systems
The post explains how extracting valuable information from data in autonomous tech and logistics boosts impact, illustrated by real-world industry examples.
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.
Navigating the challenges of data capture in mobile robotics
Outlining key challenges in real-world data capture—like occlusion, lighting, dust, and poor calibration.
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.
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.