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Multi-Sensor Late-Fusion Prototype for an Indoor AGV

FreeVerified credential3 weeksAdvanced

Overview

What this challenge is about.

Build a late-fusion LiDAR+RGB detector on KITTI data, compare it to early-fusion, and deliver a memo to earn your verifiable certificate.

The scenario

The company (around 60 staff) deploys around 40 AGVs in three EU facilities and competes on perception reliability in mixed-lighting industrial environments.

CredentialBlockchain-anchored
ShareableLinkedIn-ready
LanguageEnglish
PaceSelf-paced

The Brief

What you'll do, and what you'll demonstrate.

Recommend between early- and late-fusion sensor architectures for indoor static-obstacle detection on accuracy, latency, and maintainability.

Earning criteria — what you'll demonstrate

  • Implement two distinct sensor-fusion architectures end-to-end
  • Evaluate detection performance with per-class and latency metrics
  • Diagnose fusion-specific failure modes
  • Recommend a perception architecture with trade-offs spelled out

Program Fit

Where this fits in your program.

Sharpens the same skills your degree expects you to demonstrate.

Aligned coursework coming soon.

Careers

Career paths this challenge builds toward

Completing this challenge demonstrates skills that transfer directly to these roles:

Computer Vision Engineer

Sensor-fusion architecture bake-offs are core CV-engineer work at every AGV, drone, and AV company.

This challenge sharpens

  • sensor-fusion
  • 3d-object-detection
  • perception

Machine Learning Engineer

Disciplined comparison with per-class metrics + latency reporting is the MLE habit production teams expect.

This challenge sharpens

  • pytorch
  • benchmarking
  • ml-pipelines

Applied AI Scientist

Translating fusion comparison into a maintainability-aware recommendation is the applied AI scientist's daily output.

This challenge sharpens

  • sensor-fusion
  • benchmarking
  • perception

One more thing

You can put a credential on your CV by Friday.