Benchmark Visual SLAM Stacks for an Indoor Delivery Robot
Overview
What this challenge is about.
Run ORB-SLAM3, OpenVSLAM, and DROID-SLAM on 8 indoor rosbag recordings and measure ATE and drift. Get a verifiable certificate.
The scenario
The startup (around 35 people, 4 hospitals served in Bavaria) loses about 8 percent of delivery missions to pose drift that requires a human reset; cutting that in half would unblock the next hospital pilot.
The Brief
What you'll do, and what you'll demonstrate.
Pick the visual-SLAM stack with the best accuracy-vs-robustness trade-off for indoor hospital corridors and defend the choice with reproducible numbers.
Earning criteria — what you'll demonstrate
- Run and evaluate modern visual-SLAM systems on real robot data
- Quantify localization quality with ATE and segment-drift metrics
- Characterize failure modes in long-corridor and dynamic environments
- Defend a perception-stack choice in writing to engineering leadership
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.
- Visual Slam
Apply visual slam to solve real industry problems and demonstrate production-level capability.
- Sensor Fusion
Apply sensor fusion to solve real industry problems and demonstrate production-level capability.
- Trajectory Evaluation
Apply trajectory evaluation to solve real industry problems and demonstrate production-level capability.
- Ros
Apply ros to solve real industry problems and demonstrate production-level capability.
- Benchmarking
Apply benchmarking 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:
Computer Vision Engineer
Benchmarking visual-SLAM systems on real robot data with rigorous metrics is a core junior CV-engineer task at any indoor-robotics company; this challenge gives the student a defensible portfolio project.
This challenge sharpens
- visual-slam
- sensor-fusion
- benchmarking
ML Researcher
Designing a fair comparison across classical and learning-augmented SLAM exercises the same controlled-experiment muscle used in applied-research roles.
This challenge sharpens
- visual-slam
- benchmarking
- trajectory-evaluation
AI Engineer
Wrapping three research-grade SLAM stacks into one reproducible harness mirrors the integration work AI engineers ship at robotics startups.
This challenge sharpens
- python
- ros
- benchmarking