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September 10, 2025DronesOpen Access

A Distributed Multi-Robot Collaborative SLAM Method Based on Air–Ground Cross-Domain Cooperation

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This method demonstrates improved pose estimation in UAV and UGV teams, highlighting cross-domain cooperation.

The proposed method enables robust global pose estimation by integrating data from UAV and UGV teams, enhancing environmental perception.
By utilizing semantic and geometric features, the method significantly reduces global pose estimation errors across air and ground domains.
This approach employs a multi-level partitioning technique to optimize pose graph computations, addressing convergence issues effectively.
Experimental validations indicate that this collaborative SLAM method achieves high-precision mapping performance in benchmark datasets.

Liu et al. (2025) studied this question.

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September 10, 2025DronesOpen Access

View Full Paper

Ask AI

Bookmark

Authors

This method demonstrates improved pose estimation in UAV and UGV teams, highlighting cross-domain cooperation.

The proposed method enables robust global pose estimation by integrating data from UAV and UGV teams, enhancing environmental perception.
By utilizing semantic and geometric features, the method significantly reduces global pose estimation errors across air and ground domains.
This approach employs a multi-level partitioning technique to optimize pose graph computations, addressing convergence issues effectively.
Experimental validations indicate that this collaborative SLAM method achieves high-precision mapping performance in benchmark datasets.

Liu et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark