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Improved Dynamic Obstacle Mapping (iDOMap).

Ángel Llamazares1, Eduardo Molinos2, Manuel Ocaña1

  • 1Department of Electronics, University of Alcalá, 28801 Madrid, Spain.

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|September 30, 2020
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Summary
This summary is machine-generated.

This study enhances the Dynamic Obstacle Mapping (DOMap) system for robots and people. The improved system (iDOMap) uses LIDAR data and advanced algorithms for more robust dynamic obstacle tracking and mapping.

Keywords:
Dynamic Obstacles Mapping (DOMap)Particle Filterdynamic occlusion detectoroptical flow

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Existing Dynamic Obstacle Mapping (DOMap) systems struggle with unpredictable human movement.
  • Extended Kalman Filters (EKF) are insufficient for accurately tracking dynamic obstacles like people.
  • Robust perception is crucial for autonomous systems navigating complex environments.

Purpose of the Study:

  • To enhance the perception capabilities of the DOMap system for improved dynamic obstacle mapping.
  • To develop a more robust method for detecting and tracking both robots and people as dynamic obstacles.
  • To integrate enhanced perception into the DOMap system, creating the improved-DOMap (iDOMap).

Main Methods:

  • Utilized LIght Detection And Range (LIDAR) sensor data for environmental perception.
  • Incorporated LIDAR reflectivity remission to strengthen optical flow detection.
  • Developed static and dynamic occlusion detectors.
  • Implemented a Particle Filter (PF) for robust tracking of dynamic obstacles.

Main Results:

  • The improved-DOMap (iDOMap) system provides more robust occupancy and velocity information for dynamic obstacles.
  • Enhanced detection and tracking of both robotic and human dynamic obstacles.
  • Successfully addressed limitations of EKF in tracking unpredictable human motion.

Conclusions:

  • The iDOMap system offers a significant improvement in dynamic obstacle mapping accuracy and robustness.
  • The enhanced perception stage enables more reliable navigation and planning for autonomous systems.
  • This work provides a foundation for more sophisticated robot navigation and human-robot interaction.