Offroad Spotting Drone


The ability of drones to get a different perspective of an environment becomes useful in off-roading, where the success of the vehicle depends on choosing the perfect route through the terrain. Through building an off-road spotting drone, Mercedes hopes to achieve an engaging and safe off road experience for their SUV market.


We decided to build our drone instead of buying one because of the added control over the drone’s features and abilities.


  • PX4 drone firmware
  • LIDAR sensor for scanning


  • Raspberry Pi Server, running A* best path algorithm
  • QGroundControl open source ground control


  • React Native app
  • WebGL rendering used for creating the depth map


Mercedes is committed to improving the experience customers have with their vehicles. In order to bring more convenience and utility to the user they sponsored our team to build a drone integrated with their SUVs, to provide real-time information on the terrain that a driver cannot directly see and improve off-road safety. We utilized a drone with multiple sensors to achieve:

  • A 3D mapping of the environment.
  • An algorithm to find the best path through the terrain ahead.
  • An intuitive  app display showing obstacles and the best path to the user.




Finding and learning about the combination of technologies such as drone firmware and best path algorithms required to put together this project was our team’s greatest challenge. Ultimately we gathered a strong blend of techniques to give us a stable and reliable system.


  • Built a drone capable of taking off and landing from a trunk, scanning the ground in front of the vehicle and returning the LIDAR data back to the server.
  • Built a server for communicating between the display and drone.
  • Designed an app that is able to show a 3d model of the ground and live video from the drone with obstacle overlay, and help the user navigate the terrain.


Our drone system creates a 3d map of the environment in front of the vehicle and finds the best path through the terrain.



We would like to thank our sponsor Mercedes, Jeff Bertalotto, Kevin Gee, Utkarsh Gautam, our TAs Faeze Brahman and Chandranil Chakrabortti, and Professor Jullig for their support and mentorship.