A Coarse-to-Fine Optimization Framework for LiDAR-Based Air-Ground Cooperative Mapping

Mengchi Ai, Mohamed Elhabiby, Yandi Yang, and Naser El-Sheimy

Peer Reviewed

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. Keywords— Point clouds map, mobile mapping system, graph-based optimization, LiDAR-based SLAM, Unmanned Aerial Vehicles (UAV), Unmanned Ground Vehicles (UGV), cooperative mapping
Published in:	2025 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 28 - 1, 2025 Salt Lake Marriott Downtown at City Creek Salt Lake City, UT
Pages:	636 - 642
Cite this article:	Ai, Mengchi, Elhabiby, Mohamed, Yang, Yandi, El-Sheimy, Naser, "A Coarse-to-Fine Optimization Framework for LiDAR-Based Air-Ground Cooperative Mapping," 2025 IEEE/ION Position, Location and Navigation Symposium (PLANS), Salt Lake City, UT, April 2025, pp. 636-642.
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