Simultaneous localization and mapping techniques using a nano quadcopter

• Ano de defesa: 2024
• Autor(a) principal: Ribeiro, Laura
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Engenharia Elétrica
• Palavras-chave em Português: SLAM; Nano Quadricóptero; Nano Quadcopter; Sensor LiDAR; LiDAR sensor; Nuvem de Pontos; Point Cloud; Grade de Ocupação; Occupancy Grid; Crazyflie; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA; Engenharia elétrica; Engenharia simultânea; Robótica; Sensoriamento remoto; ODS::ODS 9. Indústria, Inovação e infraestrutura - Construir infraestrutura resiliente, promover a industrialização inclusiva e sustentável, e fomentar a inovação.
• Link de acesso: https://repositorio.ufu.br/handle/123456789/41459; http://doi.org/10.14393/ufu.di.2024.219

Simultaneous Localization and Mapping (SLAM) is a critical challenge in autonomous mobile robotics. One of the biggest challenges in engineering is to create solutions that are applicable in the real world, dealing with uncertainties, forces, and possibilities. This research navigates these seas. Therefore, this project aims to implement a system using a nano quadcopter with autonomous flight and laser sensors capable of simultaneous localization and mapping. The research aspires to present diverse techniques for solving SLAM, covering methods for reconstructing 2D and 3D maps using an open-source system with Python programming language. To this end, employing materials developed by the Bitcraze company, the system is composed of the Crazyflie 2.1 nano quadcopter and two expansion decks to enhance the drone's capabilities, Multi-ranger Deck (measurement) and the Flow Deck v2 (pose). Furthermore, it was developed a 3D map of the environment using the point cloud, and a 2D map with the occupancy grid, using distinct techniques. For the development of the methods, the 3D map and 2D map algorithms, the libraries used are Open3D, VisPy, and Matplotlib. Accomplishing the results through experiments conducted in physical environments, with diverse scenarios, four of which applied to the two strategies of flights, Scenario 1 to 4, and two of which were individual cases, Levels, and Real-Time Visualization. All the methods and solutions developed operate satisfactorily and run efficiently, with their respective characteristics and performances.