Next Article in Journal / Special Issue
An Architecture for the Integration of Robots and Sensors for the Care of the Elderly in an Ambient Assisted Living Environment
Previous Article in Journal
Heterogeneous Map Merging: State of the Art
Article

People Detection and Tracking Using LIDAR Sensors

1
Supercomputación Castilla y León (SCAyLE), Campus de Vegazana s/n, 24071 León, Spain
2
Department Mechanical, Computer Science and Aerospace Engineering, University of León, Campus de Vegazana s/n, 24071 León, Spain
3
Department Telematics and Computing (GSyC), Universidad Rey Juan Carlos, Campus de Fuenlabrada, Camino del Molino s/n, 28943 Fuenlabrada, Spain
*
Author to whom correspondence should be addressed.
Robotics 2019, 8(3), 75; https://doi.org/10.3390/robotics8030075
Received: 15 July 2019 / Revised: 7 August 2019 / Accepted: 28 August 2019 / Published: 31 August 2019
(This article belongs to the Special Issue Robotics in Spain 2019)
The tracking of people is an indispensable capacity in almost any robotic application. A relevant case is the @home robotic competitions, where the service robots have to demonstrate that they possess certain skills that allow them to interact with the environment and the people who occupy it; for example, receiving the people who knock at the door and attending them as appropriate. Many of these skills are based on the ability to detect and track a person. It is a challenging problem, particularly when implemented using low-definition sensors, such as Laser Imaging Detection and Ranging (LIDAR) sensors, in environments where there are several people interacting. This work describes a solution based on a single LIDAR sensor to maintain a continuous identification of a person in time and space. The system described is based on the People Tracker package, aka PeTra, which uses a convolutional neural network to identify person legs in complex environments. A new feature has been included within the system to correlate over time the people location estimates by using a Kalman filter. To validate the solution, a set of experiments have been carried out in a test environment certified by the European Robotic League. View Full-Text
Keywords: LIDAR; convolutional networks; people tracking; @home; robotics competitions LIDAR; convolutional networks; people tracking; @home; robotics competitions
Show Figures

Figure 1

MDPI and ACS Style

Álvarez-Aparicio, C.; Guerrero-Higueras, Á.M.; Rodríguez-Lera, F.J.; Ginés Clavero, J.; Martín Rico, F.; Matellán, V. People Detection and Tracking Using LIDAR Sensors. Robotics 2019, 8, 75. https://doi.org/10.3390/robotics8030075

AMA Style

Álvarez-Aparicio C, Guerrero-Higueras ÁM, Rodríguez-Lera FJ, Ginés Clavero J, Martín Rico F, Matellán V. People Detection and Tracking Using LIDAR Sensors. Robotics. 2019; 8(3):75. https://doi.org/10.3390/robotics8030075

Chicago/Turabian Style

Álvarez-Aparicio, Claudia, Ángel M. Guerrero-Higueras, Francisco J. Rodríguez-Lera, Jonatan Ginés Clavero, Francisco Martín Rico, and Vicente Matellán. 2019. "People Detection and Tracking Using LIDAR Sensors" Robotics 8, no. 3: 75. https://doi.org/10.3390/robotics8030075

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop