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  • 标题:People Detection and Tracking Using LIDAR Sensors
  • 本地全文:下载
  • 作者:Claudia Álvarez-Aparicio 1, ; Ángel Manuel Guerrero-Higueras ; Francisco Javier Rodríguez-Lera
  • 期刊名称:Robotics
  • 电子版ISSN:2218-6581
  • 出版年度:2019
  • 卷号:8
  • 期号:3
  • 页码:75-86
  • DOI:10.3390/robotics8030075
  • 出版社:MDPI Publishing
  • 摘要: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.
  • 关键词:LIDAR; convolutional networks; people tracking; @home; robotics competitions LIDAR ; convolutional networks ; people tracking ; @home ; robotics competitions
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