Reconocimiento de Objetos a partir de Nube de Puntos en un Ve\'iculo A\'ereo no Tripulado

TL;DR

This paper presents a framework using an autonomous aerial vehicle that maps environments and detects objects via a camera, integrating Scratch and ROS to facilitate high-level research and educational projects.

Contribution

Developed a tool combining Scratch and ROS for 3D mapping and object detection with drones, enabling accessible high-level research and education.

Findings

Successfully integrated Scratch with ROS for drone control

Created a 3D mapping and object detection tool

Facilitated high-level research using simple programming languages

Abstract

Currently, research in robotics, artificial intelligence and drones are advancing exponentially, they are directly or indirectly related to various areas of the economy, from agriculture to industry. With this context, this project covers these topics guiding them, seeking to provide a framework that is capable of helping to develop new future researchers. For this, we use an aerial vehicle that works autonomously and is capable of mapping the scenario and providing useful information to the end user. This occurs from a communication between a simple programming language (Scratch) and one of the most important and efficient robot operating systems today (ROS). This is how we managed to develop a tool capable of generating a 3D map and detecting objects using the camera attached to the drone. Although this tool can be used in the advanced fields of industry, it is also an important advance for the research sector. The implementation of this tool in intermediate-level institutions is aspired to provide the ability to carry out high-level projects from a simple programming language.