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Safe and Robust Mobile Robot Navigation in Uneven Indoor Environments.

Chaoqun Wang1, Jiankun Wang1, Chenming Li1

  • 1Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China.

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This study presents a new system for autonomous mobile robot navigation in complex 3D environments. The system enables robots to safely and efficiently navigate challenging terrains like stairs and slopes.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Autonomous mobile robot navigation in complex 3D environments presents significant challenges.
  • Existing systems often struggle with uneven terrain such as staircases and slopes.

Purpose of the Study:

  • To develop an integrated system for safe and efficient autonomous navigation in complex 3D environments.
  • To enhance robot perception and navigation capabilities within a modular framework.

Main Methods:

  • A novel Simultaneously Localization and Mapping (SLAM) framework using wheel odometry, 2D laser scanner, and RGB-D camera to build a 3D OctoMap.
  • Generation of a traversable map from multi-layer 2D maps extracted from the 3D OctoMap for navigation.
  • Incorporation of a regression forest-based camera re-localization method for stable 3D localization.
  • Utilization of a variable step size Rapidly-exploring Random Tree (RRT) method for improved navigation efficiency.

Main Results:

  • The proposed system successfully distinguishes between slopes and staircases.
  • The traversable map effectively guides robot navigation on varied 3D terrain.
  • The camera re-localization method ensures robust 3D localization.
  • The variable step size RRT method enhances navigation efficiency without manual tuning.

Conclusions:

  • The integrated system enables efficient and robust autonomous navigation for mobile robots in complex 3D environments.
  • The modular framework supports reusable perception and navigation capabilities.
  • The developed methods address key challenges in 3D robot navigation, including terrain variability and localization accuracy.