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An Adaptive Two-Dimensional Voxel Terrain Mapping Method for Structured Environment
Hang Zhou1, Peng Ping1,2, Quan Shi1
1School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China.
Sensors (Basel, Switzerland)
|December 9, 2023
Summary
This study introduces adaptive resolution 2D voxel terrain mapping (ARVTM) for foot robots in structured indoor environments. The method enhances terrain mapping accuracy by 62.7% and speed by 25.1%.
Area of Science:
- Robotics
- Computer Vision
- Artificial Intelligence
Background:
- Accurate terrain mapping is crucial for foot robot navigation and motion control.
- Existing methods may struggle with pose uncertainty and information redundancy in structured environments.
Purpose of the Study:
- To propose an adaptive resolution 2D voxel terrain mapping (ARVTM) method for indoor structured environments.
- To improve the accuracy and speed of terrain mapping for foot robots.
Main Methods:
- Constructed a terrain mapping framework incorporating robot pose estimation.
- Converted distance sensor measurements to terrain height information within a voxel grid.
- Implemented downsampling to reduce data redundancy.
- Utilized a preemptive random sample consistency (preemptive RANSAC) algorithm for plane extraction and voxel merging.
Main Results:
- Reduced terrain mapping error by 62.7%.
- Increased terrain mapping speed by 25.1%.
- Effectively identified and extracted plane features in structured environments.
Conclusions:
- The proposed ARVTM algorithm enhances terrain mapping accuracy and efficiency for foot robots.
- The method effectively handles pose uncertainty and reduces information complexity.
- ARVTM is suitable for adaptive resolution mapping in structured indoor environments.
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