Overview
What this challenge is about.
Implement a particle-filter SLAM with LiDAR and odometry from 4 ROS bags, then report ATE and CPU usage. Earn a verifiable certificate.
The scenario
The startup (about 30 people, around 90 deployed robots across India and the Gulf) loses 2-3 robots per quarter to localization failures that strand them mid-shift and require an on-site reset.
The Brief
What you'll do, and what you'll demonstrate.
Replace the AprilTag-dependent localization with a 2D-LiDAR particle-filter SLAM that's robust to dirty fiducials.
Earning criteria — what you'll demonstrate
- Apply particle-filter methods to a real state-estimation problem
- Integrate LiDAR + odometry sensor fusion in a robotics stack
- Evaluate SLAM accuracy against motion-capture ground truth
- Plan an embedded integration with realistic CPU/memory constraints
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- State Estimation
Apply state estimation to solve real industry problems and demonstrate production-level capability.
- Particle Filter
Apply particle filter to solve real industry problems and demonstrate production-level capability.
- Slam
Apply slam to solve real industry problems and demonstrate production-level capability.
- Ros
Apply ros to solve real industry problems and demonstrate production-level capability.
- Lidar
Apply lidar to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Engineer
End-to-end SLAM node delivery with embedded CPU constraints is the canonical AI-engineer project in industrial robotics.
This challenge sharpens
- slam
- ros
- state-estimation
Computer Vision Engineer
LiDAR + odometry fusion under real-world noise transfers cleanly into perception/CV engineering roles on autonomous-vehicle teams.
This challenge sharpens
- lidar
- state-estimation
- particle-filter
Machine Learning Engineer
Reproducible evaluation against motion-capture ground truth is the kind of measurement discipline MLEs need on any perception system.
This challenge sharpens
- slam
- python
- particle-filter