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H-SLAM: Rao-Blackwellized Particle Filter SLAM Using Hilbert Maps.

Guillem Vallicrosa1, Pere Ridao2

  • 1Underwater Robotics Research Center (CIRS), Computer Vision and Robotics Institute (VICOROB), Universitat de Girona, 17004 Girona, Spain. gvallicrosa@eia.udg.edu.

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Summary
This summary is machine-generated.

This study introduces H-SLAM, a new Simultaneous Localization and Mapping (SLAM) system. It efficiently creates occupancy grid maps for robot navigation and manipulation tasks using Hilbert Maps.

Keywords:
2DAUV (Autonomous Underwater Vehicle)PF (Particle Filter)SLAM (Simultaneous Localization and Mapping)

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Occupancy grid maps are crucial for robotic applications like path planning and autonomous manipulation, offering probabilistic spatial representations.
  • Existing methods may face challenges with memory footprint and online processing capabilities.

Purpose of the Study:

  • To present a novel Simultaneous Localization and Mapping (SLAM) framework, H-SLAM, designed for online generation of occupancy grid maps.
  • To leverage Hilbert Maps for a continuous, probabilistic spatial representation with reduced memory requirements.

Main Methods:

  • The H-SLAM framework utilizes Hilbert Maps for environment representation.
  • A Particle Filter is employed to manage and update the robot's state estimation.
  • The system is designed for online operation, processing data in real-time.

Main Results:

  • Experimental results from both simulated environments and real Autonomous Underwater Vehicles (AUVs) were analyzed.
  • The H-SLAM approach demonstrated consistent environment representation.
  • The system proved capable of online execution, meeting real-time processing demands.

Conclusions:

  • H-SLAM provides an effective and efficient method for generating occupancy grid maps online.
  • The use of Hilbert Maps contributes to a compact and continuous probabilistic representation.
  • The framework is suitable for robotic applications requiring real-time mapping and localization, including AUVs.