Vision-based SLAM system for MAVs in GPS-denied environments

Using a camera, a micro aerial vehicle (MAV) can perform visual-based navigation in periods or circumstances when GPS is not available, or when it is partially available. In this context, the monocular simultaneous localization and mapping (SLAM) methods represent an excellent alternative, due to se...

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Detalles Bibliográficos
Autores: Urzua, Sarquis, Munguía Alcalá, Rodrigo Francisco, Grau Saldes, Antoni|||0000-0003-4112-3325
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/105442
Acceso en línea:https://hdl.handle.net/2117/105442
https://dx.doi.org/10.1177/1756829317705325
Access Level:acceso abierto
Palabra clave:Autonomous robots
Robot vision
Micro aerial vehicles
Monocular SLAM
Visual-based navigation
GPS-denied
State estimation
Robots autònoms
Visió artificial (Robòtica)
Àrees temàtiques de la UPC::Informàtica::Robòtica
Descripción
Sumario:Using a camera, a micro aerial vehicle (MAV) can perform visual-based navigation in periods or circumstances when GPS is not available, or when it is partially available. In this context, the monocular simultaneous localization and mapping (SLAM) methods represent an excellent alternative, due to several limitations regarding to the design of the platform, mobility and payload capacity that impose considerable restrictions on the available computational and sensing resources of the MAV. However, the use of monocular vision introduces some technical difficulties as the impossibility of directly recovering the metric scale of the world. In this work, a novel monocular SLAM system with application to MAVs is proposed. The sensory input is taken from a monocular downward facing camera, an ultrasonic range finder and a barometer. The proposed method is based on the theoretical findings obtained from an observability analysis. Experimental results with real data confirm those theoretical findings and show that the proposed method is capable of providing good results with low-cost hardware.