Research on the Design and Optimization of a Multimodal Data Acquisition System for Drones in Complex Urban Environments

Siwei Han

doi:10.54254/2755-2721/2026.33981

Abstract

Amid the rapid rollout of smart city programs, drones have emerged as an indispensable platform for acquiring urban spatial information. Yet within built-up urban settings—crowded with high-rise structures, marked by pronounced GNSS signal blockage, and shaped by complicated airflow regimes—the conventional single-sensor data acquisition approach falls short of satisfying, at the same time, the requirements for precision, operational efficiency, and data completeness.To cope with the intricacies of urban built environments, this paper advances a multi-modal drone-based data acquisition system. At the hardware level, a platform integrating LiDAR, visible-spectrum cameras, and high-accuracy positioning modules is constructed. On top of that sensing stack, a spatiotemporal synchronization calibration method is introduced, built around hardware triggering and timestamp alignment .From the planning side, an adaptive path-design strategy that embeds data-quality indicators is proposed. Specifically, a multi-objective optimization model is formulated to jointly account for path length, obstacle-avoidance safety, and data acquisition quality. Once environmental changes arise, a dynamic replanning mechanism is activated, drawing on the elite-initialization idea from multi-strategy artificial swarm algorithms to raise search efficiency; third-order B-spline curves, meanwhile, are used to smooth the resulting trajectories so that dynamic constraints are satisfied. Not limited to single-platform operation, the system also includes a multi-drone collaborative acquisition mechanism, where distributed task allocation is achieved through a market-auction scheme and acquisition efficiency, together with data consistency, is strengthened through a hierarchical collaborative obstacle-avoidance framework.

Keywords: drone , multimodal data , path planning , multi-drone collaboration , complex urban environment , sensor fusion

Article Information

Received: 9 May 2026

Published: 1 June 2026

DOI: https://doi.org/10.54254/2755-2721/2026.33981

Article Type: Research Article

Cite this article

Han,S. (2026). Research on the Design and Optimization of a Multimodal Data Acquisition System for Drones in Complex Urban Environments. Applied and Computational Engineering,234,105-121.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title: ACE Vol.234

Part of Series: Applied and Computational Engineering

ISSN: 2755-2721 (Print) / 2755-273X (Online)

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[1]Mohsan S A H, et al. Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges [J]. IEEE Access, 2022, 10: 98734-98763.

[2]Pajares, G. Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs). Photogrammetric Engineering & Remote Sensing, 2015, 81(4), 281-330.

[3]Zhu J, Ren K, Fu C, et al. Online camera-lidar calibration with sensor fusion [J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1826-1833.

[4]Geiger A, Moosmann F, Car Ö, et al. Automatic camera and range sensor calibration using a single shot [C]//2012 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2012: 3936-3943.

[5]Wang H, Guo Y, Yang J, et al. A survey of path planning algorithms for unmanned aerial vehicles in complex environments [J]. IEEE Access, 2021, 9: 119326-119348.

[6]Zhou T., Tucker R., Flynn J., et al. GigaMVS: A benchmark for ultra-large-scale multi-view stereo [C] // Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024.

[7]Q. Zhu, J. Huang, and Z. Hu, "A novel multi-sensor fusion approach for UAV navigation under GNSS-denied conditions, " IEEE Trans. Veh. Technol., vol. 70, no. 9, pp. 8728–8740, Sep. 2021.

[8]J. Rehder, J. Nikolic, T. Schneider, T. Hinzmann, and R. Siegwart, "Extending kalibr: Calibration of large-scale multi-sensor systems, " in Proc. IEEE Int. Conf. Robot. Autom. (ICRA), 2016, pp. 3591–3597.

[9]O. Arslan and P. Tsiotras, "Multi-objective trajectory planning for UAVs using RRT* and weighted sum optimization, " J. Intell. Robot. Syst., vol. 96, no. 1, pp. 65–79, Oct. 2019.

[10]M. K. Al-Shabi and Z. A. Al-Yemeni, "Elite-guided artificial bee colony algorithm for global optimization, " Swarm Evol. Comput., vol. 68, p. 101014, Jan. 2022.

[11]F. Gao, L. Wang, K. Wang, W. Wu, and S. Shen, "Optimal trajectory generation for quadrotors through flatness transformation and time allocation, " IEEE Trans. Robot., vol. 37, no. 3, pp. 856–869, Jun. 2021.

[12]H. Zhao, F. Gao, and J. Pan, "Auction-based distributed task allocation for multi-UAV systems in urban environments, " IEEE Transactions on Automation Science and Engineering, vol. 19, no. 4, pp. 2971–2984, Oct. 2022.

[13]S. Liu, M. Watterson, K. Mohta, and V. Kumar, "Collision-avoiding trajectory planning for multi-robot systems using reciprocal velocity obstacles, " Int. J. Robot. Res., vol. 37, no. 13–14, pp. 1609–1633, Dec. 2018.

[14]Y. Lin, X. Huang, and R. Xiong, "Model predictive control for multi-UAV cooperative sensing with guaranteed data consistency, " IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 7750–7762, Aug. 2022.

[15]A. L. Majdik, D. Verda, and D. Scaramuzza, "Air-ground collaborative 3D reconstruction using vision-based SLAM and mesh refinement, " IEEE Robot. Autom. Lett., vol. 5, no. 4, pp. 6189–6196, Oct. 2020.