Computer Vision for Robots with OpenCV
Detect, track and follow objects from a robot's camera.
Taught by Bikash Pandey · Embedded Systems Engineer
What you'll learn
- Capture, process and display live camera frames with Python and OpenCV at a measured frame rate
- Calibrate a camera with a chessboard and correct lens distortion using the saved intrinsics
- Detect objects by colour and shape using HSV masks, morphology and contour filters
- Estimate the 3D position and orientation of ArUco markers with solvePnP
- Track a moving target across frames and smooth its position with a Kalman filter
- Convert target position into bounded, fail-safe steering commands for a differential-drive robot or ROS 2 node
About this course
This intermediate course teaches you to give a robot working eyes using Python and OpenCV. It is for makers, robotics students and embedded engineers who can already write basic Python and want to move from still images to a camera that drives motion. You will calibrate a USB or Raspberry Pi camera, remove lens distortion, detect objects by colour in HSV space, find shapes with contours, and read ArUco markers to estimate a target's position and distance in real units. You will then track objects across frames, smooth noisy measurements with a Kalman filter, and close the loop by turning pixel error into steering commands for a wheeled robot or a ROS 2 node, with the fail-safe behaviour a real robot needs. Prerequisites: comfortable Python, basic NumPy and a webcam. A laptop is enough to start; a small robot kit is optional.
Course content
7 lessons- 1How a Robot Sees: Cameras, Frames and OpenCVSet up OpenCV, grab live frames and learn the perception loop every vision-guided robot runs.Preview
- 2Camera Calibration and Lens DistortionCalibrate your camera with a chessboard so that pixels map reliably to real-world geometry.
- 3Colour Detection in HSV SpaceIsolate objects by colour with HSV thresholds, masks and morphology that cope with changing light.
- 4Shapes, Contours and Filtering DetectionsFind object outlines, classify simple shapes and reject false positives by area, aspect and solidity.
- 5ArUco Markers and Pose EstimationDetect fiducial markers and recover their 3D position and orientation relative to the camera.
- 6Tracking Objects Across FramesFollow a target with OpenCV trackers and smooth noisy positions with a Kalman filter.
- 7Closing the Loop: From Pixels to MotionTurn target position into steering and speed commands for a wheeled robot or a ROS 2 node.