Safe and Robust Mobile Robot Navigation in Uneven Indoor Environments.

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.