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| Title | A Coarse-To-Fine Optimization Framework For Lidar-Based Air-Ground Cooperative Mapping |
|---|---|
| ID_Doc | 691 |
| Authors | Ai M.; Elhabiby M.; Yang Y.; El-Sheimy N. |
| Year | 2025 |
| Published | 2025 IEEE/ION Position, Location and Navigation Symposium, PLANS 2025 |
| DOI | http://dx.doi.org/10.1109/PLANS61210.2025.11028219 |
| Abstract | Geo-referenced point cloud maps provide detailed 3D geometric features essential for a wide range of digital applications, including smart city development and automation. Generating high-accuracy and high density point cloud maps is a critical task in these domains. While stationary scanning method offer high precise measurements, their low efficiency and limited coverage make them impractical for large-scale mapping. Mobile mapping technologies, leveraging simultaneous localization and mapping (SLAM), sensor fusion, and pose estimation, address these challenges by generating point cloud maps in motion. This paper proposes a cooperative air-ground 3D mapping framework that integrates unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) to produce point cloud maps. UAVs provide large-scale aerial coverage, while UGVs deliver detailed ground-level mapping, ensuring comprehensive and accurate representation of the environment. The proposed framework follows a coarse-to-fine optimization strategy consisting of two main stages: (1) Coarse mapping - SLAM algorithms generate initial point clouds and estimate geo-referenced poses for both UAVs and UGVs. (2) Mapping optimization - Graph-based post-processing refines the poses by leveraging overlapping constraints, further improving mapping accuracy. To validate the framework, experiments were conducted using an open-source UAV-UGV dataset. The results demonstrate significant improvements in point clouds mapping and trajectory accuracy. This air-ground cooperative approach represents a scalable, robust, and accurate solution for 3D mapping applications. © 2025 IEEE. |
| Author Keywords | cooperative mapping; graph-based optimization; LiDAR-based SLAM; mobile mapping system; Point clouds map; Unmanned Aerial Vehicles (UAV); Unmanned Ground Vehicles (UGV) |
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