Fusión, clasificación y tracción simultáneas de obstáculos en movimiento mediante LIDAR y cámara usando algoritmo bayesiano

  • Masrour Dowlatabadi Islamic Azad University
  • Ahmad Afshar Amirkabir University of Technology
  • Ali Moarefianpour Islamic Azad University
Palabras clave: Mahalonobis Distance, Bayesian Algorithm, Simultaneous classification and traction, LIDAR sensor and camera

Resumen

In the near future, preventing collisions with fixed or moving, alive, and inanimate obstacles will appear to be a severe challenge due to the increased use of Unmanned Ground Vehicles (UGVs). Light Detection and Ranging (LIDAR) sensors and cameras are usually used in UGV to detect obstacles. The definite tracing and classification of moving obstacles is a significant dimension in developed driver assistance systems. It is believed that the perceived model of the situation can be improved by incorporating the obstacle classification. The present study indicated a multi-hypotheses monitoring and classifying approach, which allows solving ambiguities rising with the last methods of associating and classifying targets and tracks in a highly volatile vehicular situation. This method was tested through real data from various driving scenarios and focusing on two obstacles of interest vehicle, pedestrian.

Biografía del autor/a

Masrour Dowlatabadi, Islamic Azad University

Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

Ahmad Afshar, Amirkabir University of Technology

Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran

Ali Moarefianpour, Islamic Azad University

Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

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Publicado
2020-12-01
Sección
Artículo de investigación científica y tecnológica