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DOMapping: Multi-UAV real-time DOM mapping with local-to-global optimization.

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Title: DOMapping: Multi-UAV real-time DOM mapping with local-to-global optimization.
Authors: Tang, Li1,2 (AUTHOR), Jin, Hongqi3 (AUTHOR), Zhou, Yan1,2 (AUTHOR) zhouy825@mail.sysu.edu.cn, Liu, Xiaoping1,2 (AUTHOR)
Source: ISPRS Journal of Photogrammetry & Remote Sensing. May2026, Vol. 235, p565-583. 19p.
Subjects: SLAM (Robotics), Orthographic projection, Maps, Geospatial data
Abstract: Rapid and reliable environmental sensing is vital in natural or human-induced disasters. In such time-critical scenarios, unmanned aerial vehicle (UAV)-based mapping frameworks have become indispensable for their real-time response capabilities. Especially the rapid region-level digital orthophoto maps (DOMs) mapping, which provides up-to-date geospatial information for emergency response and decision-making. Simultaneous localization and mapping (SLAM) offers real-time pose estimation and mapping, however it suffers from trajectory drift during long-sequence UAV flights. To achieve fast and accurate real-time DOM mapping, we propose an online multi-UAV collaborative DOM mapping framework, which effectively addresses two major challenges. First, online trajectory drift and illumination changes introduce discontinuities in image orthorectification, leading to texture misalignment that compromises visual continuity in single-flight DOM mapping. Second, global spatial consistency is difficult to maintain in multi-UAV fusion, as GPS error and SLAM drift accumulate under asynchronous configuration. To address these limitations, we propose an online multi-UAV collaborative DOM mapping framework based on an inter-intra UAV interactive optimization strategy. At the intra-UAV, we propose a geographically aligned online orthorectification approach, in which a GPS–image joint optimization model integrates geodetic constraints with SLAM-derived poses to suppress drift-induced distortion and enable seamless mosaic generation during single-flight mapping. At the inter-UAV level, an online local-to-global pose optimization is performed to iteratively refine cross-flight alignment and compensate for systematic inter-UAV inconsistencies. The intra- and inter-UAV optimization processes run concurrently and mutually reinforce each other, ensuring both real-time performance and global geometric consistency during online DOM fusion.Extensive experiments on multi-UAV datasets demonstrate that our approach achieves sub-meter absolute accuracy while requiring only 60% of the computation time compared with baseline methods. The proposed framework offers a robust and scalable solution for region-scale, near real-time multi-UAV DOM mapping, offering valuable support for rapid disaster assessment, digital twins reconstruction, and other geospatial applications. The code and data are released: https://github.com/leemraz-hub/DoMapping. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Rapid and reliable environmental sensing is vital in natural or human-induced disasters. In such time-critical scenarios, unmanned aerial vehicle (UAV)-based mapping frameworks have become indispensable for their real-time response capabilities. Especially the rapid region-level digital orthophoto maps (DOMs) mapping, which provides up-to-date geospatial information for emergency response and decision-making. Simultaneous localization and mapping (SLAM) offers real-time pose estimation and mapping, however it suffers from trajectory drift during long-sequence UAV flights. To achieve fast and accurate real-time DOM mapping, we propose an online multi-UAV collaborative DOM mapping framework, which effectively addresses two major challenges. First, online trajectory drift and illumination changes introduce discontinuities in image orthorectification, leading to texture misalignment that compromises visual continuity in single-flight DOM mapping. Second, global spatial consistency is difficult to maintain in multi-UAV fusion, as GPS error and SLAM drift accumulate under asynchronous configuration. To address these limitations, we propose an online multi-UAV collaborative DOM mapping framework based on an inter-intra UAV interactive optimization strategy. At the intra-UAV, we propose a geographically aligned online orthorectification approach, in which a GPS–image joint optimization model integrates geodetic constraints with SLAM-derived poses to suppress drift-induced distortion and enable seamless mosaic generation during single-flight mapping. At the inter-UAV level, an online local-to-global pose optimization is performed to iteratively refine cross-flight alignment and compensate for systematic inter-UAV inconsistencies. The intra- and inter-UAV optimization processes run concurrently and mutually reinforce each other, ensuring both real-time performance and global geometric consistency during online DOM fusion.Extensive experiments on multi-UAV datasets demonstrate that our approach achieves sub-meter absolute accuracy while requiring only 60% of the computation time compared with baseline methods. The proposed framework offers a robust and scalable solution for region-scale, near real-time multi-UAV DOM mapping, offering valuable support for rapid disaster assessment, digital twins reconstruction, and other geospatial applications. The code and data are released: https://github.com/leemraz-hub/DoMapping. [ABSTRACT FROM AUTHOR]
ISSN:09242716
DOI:10.1016/j.isprsjprs.2026.03.032