The objective of this project was to get a monocular quad copter to align to ARuco markers  and reference its position and vectors using the camera sensors and reference position alignment in space. We created a PID algorithm that checks and corrects vector alignment error.


Challenges :

  • Some documentation might be outdated and not maintained anymore.
  • Difficulties installing libraries and maintain compatibility.
  • Ubuntu breaks easily and adds difficulties on driver installation
  • Most instructions are just understandable with experience with Ubuntu and ROS.

Sequence Diagram : 

Results :

  • Control the Drone with the keyboard
  • OpenCV Edge detection
  • OpenCV-Extraction red light from the camera in order to measure the pixels
  • ARuco-Marker Detection with Bebop Camera
  • Realignment of a Pan&Tilt system with laser projector to the ARuco marker


Description of the solution :

Parallel alignment allows better flexibility aligning to the orientation constraints, by detecting the normal vectors and align to the surface to obtain a better sharpness of the projection, maintaining an optimal focus in the laser.

This simple solution, is a first step to achieve better object recognition and obstacle avoidance.

Learned Topics, used technologies or algorithm used :

  • Ubuntu OS environment.
  • ROS basics.
  • Terminal Operations and jargon.
  • Computer Vision basics: Opencv:     1. Camera Calibration   a. Marker Detection & b. Distance Recognition      2. Edge Detection
  • G-Code to HEX-Code for Cartesian projections.
  • Tests of calculating the distance with OpenCV with the projected laserlight 

Application : 

For the construction industry there are many possible applications for this idea. First of all you can project AR for a bigger group of people on building site to visualize a floorplan, building informations or simple measurements without the need of expensive AR-Glasses for every single persons.

Image : Shape Projection

Next steps:

  • Have the alignment algorithm fully working.
  • Combine laser implementations with the monocular camera vision of the drone.
  • Have a SLAM implementation in the node.
  • Laser scanning & Object Recognition
  • Obstacle Avoiding & Distortion Correction

Potential Improvements:

  • Create a dynamic range of projections working in combination of the object recognition and possibly with BIM.

Laser Drone is a project of IaaC, Institute for Advanced Architecture of Catalonia developed at Master in Robotics and Advanced Construction (M.R.A.C.) in 2019 by,

Student:  Subhash Prajapat, Stefano Meloni, Sebastian Voigt, Luis Pacheco

Faculty: Daniel Serrano & Jose Starsk Lara