Overview
What this challenge is about.
Calibrate a Multi-Camera Rig for Warehouse Robotics. Intermediate challenge in code. Writing production code that solves real engineering problems, earn a bl...
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.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
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