In our OpenCV camera calibration tutorial, we show how to get started with OpenCV by setting up the software, printing and capturing a calibration chessboard, and then calibrate the camera using the captured images.
The article explains how to implement a simple vision-based navigation system using OpenCV and Python. It demonstrates a toy example where position metadata is encoded into floor colors, with red and green channels representing X and Y coordinates. Through code examples, the article walks through creating this artificial environment, capturing data with a camera, and processing it to estimate position, orientation, and more advanced navigation techniques.
Vision Navigation is a blanket term that describes using optical-like raster input devices to discover your position and orientation (“pose”) and then use that position to plan, execute and monitor changes in position/pose within the environment. This whitepaper introduces the techniques, strengths and weaknesses, applications and processes of computer vision navigation.
