Tracking People in a Mobile Robot From 2D LIDAR Scans Using Full Convolutional Neural Networks for Security in Cluttered Environments

[EN] Tracking people has many applications, such as security or safe use of robots. Many onboard systems are based on Laser Imaging Detection and Ranging (LIDAR) sensors. Tracking peoples' legs using only information from a 2D LIDAR scanner in a mobile robot is a challenging problem because man...

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Detalles Bibliográficos
Autores: Guerrero Higueras, Ángel Manuel, Álvarez Aparicio, Claudia, Calvo Olivera, María Carmen, Rodríguez Lera, Francisco Javier, Fernández Llamas, Camino, Martín Rico, Francisco, Matellán Olivera, Vicente
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universidad Rey Juan Carlos
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/18201
Acceso en línea:https://www.frontiersin.org/articles/10.3389/fnbot.2018.00085/full#h8
https://hdl.handle.net/10612/18201
Access Level:acceso abierto
Palabra clave:Cibernética
Convolutional networks
LIDAR
People tracking
Robotics
Cluttered environments
System
1207.03 Cibernética
Descripción
Sumario:[EN] Tracking people has many applications, such as security or safe use of robots. Many onboard systems are based on Laser Imaging Detection and Ranging (LIDAR) sensors. Tracking peoples' legs using only information from a 2D LIDAR scanner in a mobile robot is a challenging problem because many legs can be present in an indoor environment, there are frequent occlusions and self-occlusions, many items in the environment such as table legs or columns could resemble legs as a result of the limited information provided by two-dimensional LIDAR usually mounted at knee height in mobile robots, etc. On the other hand, LIDAR sensors are affordable in terms of the acquisition price and processing requirements. In this article, we describe a tool named PeTra based on an off-line trained full Convolutional Neural Network capable of tracking pairs of legs in a cluttered environment. We describe the characteristics of the system proposed and evaluate its accuracy using a dataset from a public repository. Results show that PeTra provides better accuracy than Leg Detector (LD), the standard solution for Robot Operating System (ROS)-based robots.