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A Review on Map-Merging Methods for Typical Map Types in Multiple-Ground-Robot SLAM Solutions
Shuien Yu1, Chunyun Fu1, Amirali K Gostar2
1State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel, Switzerland)
|December 10, 2020
Summary
Map merging is crucial for multi-robot simultaneous localization and mapping (SLAM). This review classifies and elaborates on map-merging methods for occupancy grid, feature-based, and topological maps to guide future solutions.
Area of Science:
- Robotics
- Artificial Intelligence
- Computer Vision
Background:
- Multi-robot systems require integrated environmental representations.
- Simultaneous Localization and Mapping (SLAM) is essential for robot navigation.
- Merging local maps from individual robots improves global map accuracy.
Purpose of the Study:
- To review and classify map-merging methods for multi-robot SLAM.
- To analyze common map types: occupancy grid, feature-based, and topological maps.
- To provide guidance for future multi-robot SLAM development.
Main Methods:
- Classification of map-merging approaches based on mechanism and features.
- Elaboration on the concepts and characteristics of various map-merging techniques.
- Review of methods applicable to occupancy grid, feature-based, and topological maps.
Main Results:
- Identified key map-merging methods for different map representations.
- Categorized approaches by their underlying principles and feature handling.
- Detailed explanation of the strengths and weaknesses of each method.
Conclusions:
- Effective map merging is critical for successful multi-robot SLAM.
- Understanding different map-merging strategies is vital for system performance.
- This review offers a foundational resource for researchers and developers in multi-robot mapping.
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