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2026 Vol.14, Issue 2 Preview Page
Development of an Embedded LIO-SAM Based High Precision 3-Dimensional Point-Map Generation Model for Autonomous Photovoltaic O&M Drones

태양광 O&M 자율주행 드론을 위한 임베디드 LIO-SAM 기반의 고정밀 3차원 포인트맵 생성 모델 개발

Hyun Min Lee1
,
Changjo Kim2*
,
Byeongsu Kim2*
이 현민1
,
김 창조2*
,
김 병수2*
1Department of Electrical, Electronic, and Computer Engineering engineering, University of Ulsan, Ulsan, 44610, Korea
2School of Electrical and Electronic Engineering, University of Ulsan, Ulsan, 44610, Korea
1전기전자정보시스템공학전공, 울산대학교, 울산, 44610
2전기전자융합학부, 울산대학교, 울산, 44610
*Corresponding Author
30 June 2026. pp. 64-76
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Abstract

Efficient Operations and Maintenance (O&M) technology is essential for the commercialization of solar power plants. However, repetitive geometries and GNSS-denied environments in solar plants remain critical challenges for real-time and highly accurate point-map generation. Herein, we develop an embedded-based LIO-SAM mapping model for precise solar array inspection on the NVIDIA Jetson Xavier NX. We systematically investigate the effects of input density (downsampleRate), feature validation (SurfFeatureMinValidNum), and mapping resolution (LeafSize) on mapping quality and latency. Our observations suggest that the downsampleRate of 10 improves mapping precision by 21.7% while reducing noise-induced standard deviation by 78%. The optimization of SurfFeatureMinValidNum to 500 decreases edge planarity error by 51.6%, ensuring structural fidelity. Furthermore, setting LeafSize to 0.2 reduces processing delay by approximately 27%, identified as the critical saturation point for efficiency. Consequently, our 3-dimensional point-map model achieves a high precision of 99.6% with a 6.2 mm error margin, demonstrating robust real-time mapping capabilities suitable for autonomous drone system.

Keywords
Solar power plant O&M
3-dimensional point map
LiDAR-Inertial SLAM
LIO-SAM
Embedded system optimization
References
1

A. O. M. Maka, J. M. Alabid, Solar energy technology and its roles in sustainable development. Clean Energy 6, 476-483 (2022).

10.1093/ce/zkac023
2

W. Shin, J. Lee, Y. Ju, H. Hwang, S. Ko, Analysis of output characteristics of solar modules according to long-term reliability test, Proc. of the KSES 2025 Annual Spring Conference, Boryeong, Korea, 212 (2025).

3

G. Di Lorenzo, R. Araneo, M. Mitolo, A. Niccolai, F. Grimaccia, Review of O&M practices in PV plants: Failures, solutions, remote control, and monitoring tools. IEEE J. Photovolt. 10, 914-926 (2020).

10.1109/JPHOTOV.2020.2994531
4

G. Rediske, L. Michels, J. C. M. Siluk, P. D. Rigo, C. B. Rosa, A. C. Lima, A proposed set of indicators for evaluating the performance of the operation and maintenance of photovoltaic plants. Appl. Energy 354, 122158 (2024).

5

H. W. Ahn, Study on Operation and Maintenance Certification Policy of Photovoltaic Power Plant for Vitalization of Photovoltaic Industry, Master’s thesis, Korea University (2015).

6

H. Abdulla, A. Sleptchenko, A. Nayfeh, Photovoltaic systems operation and maintenance: A review and future directions. Renew. Sustain. Energy Rev. 195, 114342 (2024).

10.1016/j.rser.2024.114342
7

A. Walker, E. Lockhart, J. Desai, K. Ardani, G. Klise, O. Lavrova, T. Tansy, J. Deot, B. Fox, A. Pochiraju, Model of Operation-and-Maintenance Costs for Photovoltaic Systems (Technical Report NREL/TP-5C00-74840, National Renewable Energy Laboratory (NREL), 2020).

10.2172/1659995
8

W. S. Kim, S. J. Park, S. R. An, Analysis of the status of safety management of photovoltaic power generation facilities. Curr. Photovolt. Res. 7, 38-45 (2019).

10.21218/CPR.2019.7.2.038
9

K. D. Lee, D. O. Lim, Establishment and Operation of Long-Term LCOE Forecast System for Expansion of Renewable Energy (5/5) (General Research 2024-22, Korea Energy Economics Institute (KEEI), 2024).

10

J. C. Duroha, G. A. Macht, Solar installation occupational risks: A systematic review. Saf. Sci. 160, 105918 (2022).

10.1016/j.ssci.2022.106048
11

A. Sen, A. S. Mohankar, A. Khamaj, S. Karmakar, Emerging OSH issues in installation and maintenance of floating solar photovoltaic projects and their link with sustainable development goals. Risk Manag. Healthc. Policy 14, 1939-1957 (2021).

10.2147/RMHP.S30473234012306PMC8128445
12

B. K. Sovacool, M. Kryman, E. Laine, Profiling technological failure and disaster in the energy sector: A comparative analysis of historical energy accidents. Energy 90, 2016-2027 (2015).

10.1016/j.energy.2015.07.043
13

M. Cubukcu, A. Akanalci, Real-time inspection and determination methods of faults on photovoltaic power systems by thermal imaging in Turkey. Renew. Energy 147, 1231-1238 (2020).

10.1016/j.renene.2019.09.075
14

S.-J. Oh, C.-M. Kim, J.-H. Jang, Failure mode analysis for accident prevention in PV systems. J. Korean Solar Energy Soc. 44, 105-115 (2024).

10.7836/kses.2024.44.6.105
15

D. Nilsson, Fault detection in photovoltaic systems, Master’s thesis, KTH Royal Institute of Technology (2014).

16

E. H. Sepúlveda-Oviedo, B. Estibals, Framework for effective PV system instrumentation focused on fault diagnosis. Sol. Energy Adv. 5, 100112 (2025).

10.1016/j.seja.2025.100112
17

B. Figgis, S. I. Azid, D. Parlevliet, Review of unmanned ground vehicles for PV plant inspection. Sol. Energy 291, 113404 (2025).

10.1016/j.solener.2025.113404
18

J. Albihn, Human Factors in Aerial Drone Operations, Bachelor’s Thesis, Lund University (2022).

19

M. Tradacete-Ágreda, C. Santos-Pérez, P. J. Hueros-Barrios, F. J. Rodríguez-Sánchez, P. Campoy, R. Pérez-Seguí, J. Melero-Deza, Framework for autonomous inspection of PV plants using IoT electronics on each PV panel and UAV collaboration. Energy Convers. Manag.: X, 26, 100878 (2025).

10.1016/j.ecmx.2025.100878
20

M. G. Al-Gawda, C. Y. Lau, N. S. Lai, Autonomous UAV inspection system for preventive maintenance of solar farms. J. Eng. Sci. Technol. Special Issue on SIET2022, 132-156 (2022).

21

K. Wang, L. Kooistra, R. Pan, W. Wang, J. Valente, UAV-based simultaneous localization and mapping in outdoor environments: A systematic scoping review. J. Field Robot. 41, 1617-1642 (2024).

10.1002/rob.22325
22

K. Long, Deep learning and visual SLAM for autonomous navigation of UAVs: Status, challenges, and future perspectives. Highlights Sci. Eng. Technol. 119, 746-751 (2024).

10.54097/wyseqa62
23

S. Vélez, J. Valente, T. Bretzel, M. Trommsdorff, Assessing the impact of overhead agrivoltaic systems on GNSS signal performance for precision agriculture. Smart Agric. Technol. 9, 100664 (2024).

10.1016/j.atech.2024.100664
24

H. Gil, D. Lee, G. Song, S. Ahn, A. Kim, Tightly-coupled GNSS-LiDAR-inertial state estimator for mapping and autonomous driving. J. Korea Robot. Soc. 18, 72-81 (2023).

10.7746/jkros.2023.18.1.072
25

G. MacGougan, G. Lachapelle, R. Nayak, A. Wang, Overview of GNSS signal degradation phenomena, Proc. Int. Symp. Kinematic Syst. Geodesy, Geomatics Navig. (KIS), Banff, Canada, 87-100 (2001).

26

S. Jung, D. Choi, S. Song, H. Myung, Autonomous navigation system of an unmanned aerial vehicle for structural inspection. J. Korea Robot. Soc. 16, 216-222 (2021).

10.7746/jkros.2021.16.3.216
27

M. H. Shahna, E. Haaparanta, P. Mustalahti, J. Mattila, LiDAR-Inertial SLAM-Based navigation and Safety-Oriented AI-Driven Control System for Skid-Steer Robots. arXiv preprint arXiv:2505.02598 (2025).

10.1109/CDC57313.2025.11312436
28

Y. He, Y. Gao, Y. Liu, X. Qu, LiDAR-IMU SLAM framework in autonomous modular bus docking systems. Green Energy Intell. Transp. 4, 100343 (2025).

10.1016/j.geits.2025.100343
29

T. Shan, B. Englot, D. Meyers, W. Wang, C. Ratti, D. Rus, LIO-SAM: Tightly-coupled lidar inertial odometry via smoothing and mapping, Proc. of IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), LV, USA, 5135-5142, 2020).

10.1109/IROS45743.2020.9341176
30

Y. Paeng, S. Lee, Performance Comparison of LIO-SAM and FAST-LIO2 for Real-Time Mapping in Autonomous Vehicles, Proc. of the KSME 2025 Spring Conference, Gyeongju, Korea, KSME25IT-Th01P15 (2025).

31

S. Jung, A. Kim, Out door map feature point generating mechanism design and algorithm robust to lidar sparsity. J. Constr. Autom. Robot. 1, 10-15 (2022).

10.55785/JCAR.1.1.10
32

D. Kim, K. Lee, K. Khoshelham, H. Shin, W. H. Lee, Quantitative evaluation of point cloud deformations on glass surfaces caused by unmanned aerial vehicle light detection and ranging incidence angle. Sens. Mater. 37(6), 2547-2568 (2025).

10.18494/SAM5362
33

S. Salehi Rad, Z. Zheng, R. Kheirollahi, A. Mostafa, S. Zhao, Y. Wang, J. Chevinly, E. Nadi, T. Bensala, H. Zhang, G. Friedman, F. Lu, Electromagnetic interference on unmanned aerial vehicles (UAVs): A case study of high power transmission line impacts. IEEE Trans. Transp. Electrif. 11(3), 7501-7513 (2025).

10.1109/TTE.2025.3528188
34

Q. Picard, S. Chevobbe, M. Darouich, J.-Y. Didier, A survey on real-time 3D scene reconstruction with SLAM methods in embedded systems. arXiv preprint arXiv:2309.05349 (2023).

Information
  • Publisher :Korea Photovoltaic Society
  • Publisher(Ko) :한국태양광발전학회
  • Journal Title :Current Photovoltaic Research
  • Volume : 14
  • No :2
  • Pages :64-76
  • Received Date : 2025-12-05
  • Revised Date : 2026-01-19
  • Accepted Date : 2026-01-20
  • DOI :https://doi.org/10.21218/CPR.2026.14.2.064
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