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
Open AccessArticle

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.

Show more citation formats Show less citations formats
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