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A Hybrid Visual-Based SLAM Architecture: Local Filter-Based SLAM with KeyFrame-Based Global Mapping.

Rodrigo Munguia1, Juan-Carlos Trujillo1, Edmundo Guerra2

  • 1Department of Computer Science (CUCEI), University of Guadalajara, Guadalajara 44430, Mexico.

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Summary
This summary is machine-generated.

This study introduces a hybrid Simultaneous Localization and Mapping (SLAM) architecture, combining filter-based and optimization-based methods for robust visual SLAM. This approach enhances map consistency and loop closure for unmanned aerial vehicles.

Keywords:
filterglobal mappinghybridkey-framelocal mappingloop closureoptimizationvisual SLAM

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Simultaneous Localization and Mapping (SLAM) systems are crucial for autonomous navigation.
  • Current visual-based SLAM systems predominantly use either filter-based or optimization-based techniques, each with inherent strengths and weaknesses.
  • Integrating these methodologies offers a potential pathway to overcome individual limitations.

Purpose of the Study:

  • To propose a novel hybrid visual-based SLAM architecture.
  • To leverage the advantages of both filter-based and optimization-based SLAM methodologies.
  • To enhance map consistency and address the loop closure problem in SLAM.

Main Methods:

  • A hybrid architecture combining filter-based techniques for local SLAM and optimization-based techniques for global map building and loop closure.
  • Implementation of a monocular-based SLAM system for unmanned aerial vehicles (UAVs).
  • Integration of additional sensory inputs into the SLAM system.

Main Results:

  • The proposed hybrid architecture effectively integrates filter-based and optimization-based SLAM approaches.
  • Successful implementation of a monocular SLAM system for UAVs with integrated sensors.
  • Experimental validation using real-world data from a quadrotor demonstrates the feasibility of the approach.

Conclusions:

  • The hybrid SLAM architecture offers a promising solution for robust and consistent environmental mapping.
  • This approach can be adapted for various visual-based SLAM system variants.
  • The demonstrated system is suitable for applications like autonomous navigation in UAVs.