Overview
What this challenge is about.
Calibrate a warehouse robotics multi-camera rig with a ChArUco board, then document the pipeline. Get a verifiable certificate.
The scenario
The startup operates a fleet of about 400 robots across 12 warehouses in the Benelux region; calibration drift currently triggers around 30 service tickets per month at roughly EUR 400 per truck-roll.
The Brief
What you'll do, and what you'll demonstrate.
Build a 15-minute, technician-runnable calibration pipeline that brings a four-camera rig back to sub-pixel reprojection accuracy and sub-degree inter-camera pose error.
Earning criteria — what you'll demonstrate
- Apply the pinhole camera model and lens-distortion coefficients to real imagery
- Estimate camera extrinsics from shared fiducial observations across views
- Validate calibration quality via reprojection error and pose-consistency checks
- Translate a research-grade pipeline into a field-deployable tool
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
3D Vision and Multi-View Geometry
Master · Computer Vision
Strong alignment
This challenge maps to 3D Vision and Multi-View Geometry at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Camera Calibration
Apply camera calibration to solve real industry problems and demonstrate production-level capability.
- Multi View Geometry
Apply multi view geometry to solve real industry problems and demonstrate production-level capability.
- Opencv
Apply opencv to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Image Processing
Apply image processing to solve real industry problems and demonstrate production-level capability.
- Tooling Design
Apply tooling design to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Machine Learning Engineer
Packaging a perception preprocessing step as a reliable, reproducible pipeline mirrors how MLEs ship feature pipelines: deterministic outputs, error budgets, and runbooks for ops.
This challenge sharpens
- python
- tooling-design
- image-processing