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Open AccessArticle

Improved Dynamic Obstacle Mapping (iDOMap)

1
Department of Electronics, University of Alcalá, 28801 Madrid, Spain
2
Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
3
Institute of Perception, Action and Behavior, University of Edinburgh, Edinburgh EH8 9YL, UK
*
Author to whom correspondence should be addressed.
Current address: Escuela Politécnica Superior, Campus Universitario s/n, Alcalá de Henares, Madrid, Spain.
Sensors 2020, 20(19), 5520; https://doi.org/10.3390/s20195520
Received: 26 August 2020 / Revised: 18 September 2020 / Accepted: 22 September 2020 / Published: 26 September 2020
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
The goal of this paper is to improve our previous Dynamic Obstacle Mapping (DOMap) system by means of improving the perception stage. The new system must deal with robots and people as dynamic obstacles using LIght Detection And Range (LIDAR) sensor in order to collect the surrounding information. Although robot movement can be easily tracked by an Extended Kalman Filter (EKF), people’s movement is more unpredictable and it might not be correctly linearized by an EKF. Therefore, to deal with a better estimation of both types of dynamic objects in the local map it is recommended to improve our previous work. The DOMap has been extended in three key points: first the LIDAR reflectivity remission is used to make more robust the matching in the optical flow of the detection stage, secondly static and a dynamic occlusion detectors have been proposed, and finally a tracking stage based on Particle Filter (PF) has been used to deal with robots and people as dynamic obstacles. Therefore, our new improved-DOMap (iDOMap) provides maps with information about occupancy and velocities of the surrounding dynamic obstacles (robots, people, etc.) in a more robust way and they are available to improve the following planning stage. View Full-Text
Keywords: Dynamic Obstacles Mapping (DOMap); Particle Filter; optical flow; dynamic occlusion detector Dynamic Obstacles Mapping (DOMap); Particle Filter; optical flow; dynamic occlusion detector
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Llamazares, Á.; Molinos, E.; Ocaña, M.; Ivan, V. Improved Dynamic Obstacle Mapping (iDOMap). Sensors 2020, 20, 5520.

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