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Active Mapping and Robot Exploration: A Survey.

Iker Lluvia1, Elena Lazkano2, Ander Ansuategi1

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Summary
This summary is machine-generated.

This review covers simultaneous localization and mapping (SLAM) for robots. It focuses on autonomous navigation and mapping strategies to minimize errors in unknown environments, particularly for indoor mobile robotics.

Keywords:
explorationfrontiersmappingmobile robotsnext best viewpath planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Simultaneous Localization and Mapping (SLAM) addresses the challenge of creating environment maps without prior data.
  • Robots often rely on human operators, but autonomous navigation while mapping, known as native SLAM, is an emerging capability.

Purpose of the Study:

  • To provide a comprehensive review of research in simultaneous localization and mapping.
  • To highlight key contributions in the field of indoor mobile robotics.

Main Methods:

  • Reviewing existing research and literature on SLAM techniques.
  • Analyzing strategies for autonomous navigation and map building.

Main Results:

  • Identified native SLAM as a key strategy for autonomous exploration and mapping.
  • Focused on minimizing mapping errors through active trajectory calculation.

Conclusions:

  • SLAM is crucial for robots operating in unknown environments.
  • Native SLAM offers advanced capabilities for autonomous indoor mobile robotics.