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Object-Based Reliable Visual Navigation for Mobile Robot.

Fan Wang1,2, Chaofan Zhang1, Wen Zhang1

  • 1Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.

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Summary
This summary is machine-generated.

This study introduces a new object-level topological visual navigation method for robots, reducing reliance on precise maps. The approach enhances navigation reliability and efficiency in real-world scenarios.

Keywords:
Bernstein polynomialobject-level topological semantic maptopological path planningvisual navigation

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Visual navigation is crucial for autonomous mobile robots.
  • Existing methods often require precise metric maps or extensive training data.
  • Reliability and efficiency in visual navigation remain significant challenges.

Purpose of the Study:

  • To propose a novel object-level topological visual navigation method.
  • To enhance the reliability and generality of visual navigation systems.
  • To reduce dependence on precise metric maps and large training datasets.

Main Methods:

  • Constructing a lightweight object-level topological semantic map using graph memory.
  • Employing an object-based heuristic graph search for optimal path selection.
  • Implementing a global path segmentation strategy based on active perception and object guidance.
  • Utilizing Bernstein polynomial-based smooth trajectory refinement for adaptive navigation.

Main Results:

  • Demonstrated feasibility and efficiency in both simulation and real-world environments.
  • Achieved improved navigation success rate (SR) and success weighted by inverse path length (SPL).
  • Outperformed state-of-the-art methods in key navigation performance metrics.

Conclusions:

  • The proposed object-level topological visual navigation method is effective and reliable.
  • The approach offers a viable alternative to metric map-dependent and data-hungry methods.
  • This work contributes to advancing autonomous mobile robot navigation capabilities.