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Autonomous Drone Mapping

Team Full

Developing sim-to-real pipeline for autonomously exploring and mapping unknown 3D environments with a drone.

Project Manager
Nathan Miller

Computer Vision Process Control Robotics
Ran
Running since Spring 2026
Commitment
5-7 hrs/week
Openings
3-4

What you need coming in

Proficiency in Python, including PyTorch. Comfort with Linux CLI, VMs, and Docker. Foundational knowledge of reinforcement learning. Robotics experience (FIRST, school organizations, or similar). Commitment and a willingness to learn.

We aim to develop a simulation-first autonomous drone pipeline that maps real-world environments. The primary goal is completing a drone navigation stack using CAD modeling, physics simulations, SLAM, and reinforcement learning path planning. Once the drone system works, we plan to coordinate and optimize a swarm of drones to map virtual spaces simultaneously. Research on these systems is independently scattered; our innovation lies in combining them into a unified project optimizing for coverage completeness, energy efficiency, and flight time.

Last semester, we built individual pieces of this pipeline that passed data between layers via recorded ROS bags. This semester’s objective is a codebase transferable to physical hardware. By the end of the term we plan to demonstrate a single drone autonomously exploring unknown simulated environments while constructing real-time 3D maps.

Technical elements

  • SLAM pipeline: FAST-LIO2, RTAB-Map for real-time localization with loop closure
  • RL navigation: Proximal Policy Optimization (PPO)
  • Multi-drone coordination: decentralized frontier-based exploration with Hungarian algorithm task allocation and ORCA collision avoidance
  • Simulation stack: Isaac Sim/Lab

Published research advancing robotics SLAM systems, including FAST-LIO2 and RTAB-Map, is the culmination of years of doctoral work. This project distills realistic milestones while maintaining technical rigor, giving members research-adjacent experience that bridges classroom knowledge and industry-level robotics engineering.