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Título
Tracking People in a Mobile Robot From 2D LIDAR Scans Using Full Convolutional Neural Networks for Security in Cluttered Environments
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Facultad/Centro
Área de conocimiento
Título de la revista
Frontiers in Neurorobotics
Datos de la obra
Guerrero-Higueras, Á. M., Álvarez-Aparicio, C., Calvo Olivera, M. C., Rodríguez-Lera, F. J., Fernández-Llamas, C., Rico, F. M., & Matellán, V. (2019). Tracking people in a mobile robot from 2D lidar scans using full convolutional neural networks for security in cluttered environments. Frontiers in Neurorobotics, 13. https://doi.org/10.3389/FNBOT.2018.00085
Editor
Frontiers Media
Fecha
2019-01-08
Résumé
[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.
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