Research Article
BibTex RIS Cite

Accuracy comparison of mobile mapping system for road inventory

Year 2023, Volume: 5 Issue: 2, 55 - 66, 15.12.2023

Hüseyin Kurşun

https://doi.org/10.53093/mephoj.1334286

Abstract

Mobile Mapping Systems (MMSs) stand out as the preferred solution for achieving highly precise 3D environmental models, particularly in urban planning, highway mapping, asset inventory, corridor mapping, traffic safety evaluation, autonomous vehicle, digital twin, and emergency response mapping where traditional aerial or satellite surveys often fall short in providing precise data. Understanding the intricate factors that impact the accuracy of mobile mapping is pivotal to harnessing the full potential of this advanced measurement technique. This study analyzes the spatial accuracy of geographical data produced by the mobile mapping method, considering factors such as the speed of the mobile mapping tool, measurement time difference, camera shooting distance of the produced data, and differences in picture shooting distances. The acquired results were examined for their applicability in the production of inventory along the highway route, revealing their practical usability through analysis and findings. This investigation delves into the proficiency and precision benchmarks of mobile mapping systems, specifically in the context of creating road inventory and supporting decision-making for road systems. The study discusses the usability and accuracy criteria of mobile mapping systems for creating transportation inventory and decision support systems.

Keywords

Mobile Mapping System Digital Twin High-Definition Maps Photogrammetry Road Inventory

References

  • Natsui, R. K., Mireku, K. K., Amuzu, G. G. K., & Sasu, E. (2022, June). An Integrated Geographical Information and Road Asset Management System for road transport network sustainability in developing countries. 28th International Conference on Engineering, Technology and Innovation (ICE/ITMC) & 31st International Association for Management of Technology (IAMOT) Joint Conference (pp. 1-6). IEEE. https://doi.org/10.1109/ICE/ITMC-IAMOT55089.2022.10033144
  • Keleş, M. D., & Aydin, C. C. (2020). Mobil lidar verisi ile kent ölçeğinde cadde bazlı envanter çalışması ve coğrafi sistemleri entegrasyonu-Ankara Örneği. Geomatik, 5(3), 193-200. https://doi.org/10.29128/geomatik.643569
  • Elhashash, M., Albanwan, H., & Qin, R. (2022). A review of mobile mapping systems: From sensors to applications. Sensors, 22(11), 4262. https://doi.org/10.3390/s22114262
  • Luo, H., Wang, C., Wen, C., Cai, Z., Chen, Z., Wang, H., ... & Li, J. (2015). Patch-based semantic labeling of road scene using colorized mobile LiDAR point clouds. IEEE Transactions on Intelligent Transportation Systems, 17(5), 1286-1297. https://doi.org/10.1109/TITS.2015.2499196
  • Yang, B., Dong, Z., Zhao, G., & Dai, W. (2015). Hierarchical extraction of urban objects from mobile laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing, 99, 45-57. https://doi.org/10.1016/j.isprsjprs.2014.10.005
  • Wen, C., Li, J., Luo, H., Yu, Y., Cai, Z., Wang, H., & Wang, C. (2015). Spatial-related traffic sign inspection for inventory purposes using mobile laser scanning data. IEEE Transactions on Intelligent Transportation Systems, 17(1), 27-37. https://doi.org/10.1109/TITS.2015.2418214
  • Wu, Y., Wang, Y., Zhang, S., & Ogai, H. (2020). Deep 3D object detection networks using LiDAR data: A review. IEEE Sensors Journal, 21(2), 1152-1171. https://doi.org/10.1109/JSEN.2020.3020626
  • Broggi, A. (1995, September). A massively parallel approach to real-time vision-based road markings detection. In Proceedings of the Intelligent Vehicles' 95, 84-89. https://doi.org/10.1109/IVS.1995.528262
  • He, Y., Wang, H., & Zhang, B. (2004). Color-based road detection in urban traffic scenes. IEEE Transactions on intelligent transportation systems, 5(4), 309-318. https://doi.org/10.1109/TITS.2004.838221
  • Veit, T., Tarel, J. P., Nicolle, P., & Charbonnier, P. (2008, October). Evaluation of road marking feature extraction. In 2008 11th International IEEE Conference on Intelligent Transportation Systems, 174-181. https://doi.org/10.1109/ITSC.2008.4732564
  • Grejner-Brzezinska, D. A., Li, R., Haala, N., & Toth, C. (2004). From Mobile Mapping to Telegeoinformatics. Photogrammetric Engineering & Remote Sensing, 70(2), 197-210. https://doi.org/10.14358/PERS.70.2.197
  • Chen, S., Chen, F., Liu, J., Wu, J., & Bienkiewicz, B. (2010). Mobile mapping technology of wind velocity data along highway for traffic safety evaluation. Transportation research part C: emerging technologies, 18(4), 507-518. https://doi.org/10.1016/j.trc.2009.10.003
  • Li, R. (1997). Mobile mapping: An emerging technology for spatial data acquisition. Photogrammetric Engineering and Remote Sensing, 63(9), 1085-1092.
  • Poggenhans, F., Pauls, J. H., Janosovits, J., Orf, S., Naumann, M., Kuhnt, F., & Mayr, M. (2018, November). Lanelet2: A high-definition map framework for the future of automated driving. In 2018 21st international conference on intelligent transportation systems (ITSC), 1672-1679. https://doi.org/10.1109/ITSC.2018.8569929
  • Otero, R., Lagüela, S., Garrido, I., & Arias, P. (2020). Mobile indoor mapping technologies: A review. Automation in Construction, 120, 103399. https://doi.org/10.1016/j.autcon.2020.103399
  • Errandonea, I., Beltrán, S., & Arrizabalaga, S. (2020). Digital Twin for maintenance: A literature review. Computers in Industry, 123, 103316. https://doi.org/10.1016/j.compind.2020.103316
  • Yu, G., Wang, Y., Mao, Z., Hu, M., Sugumaran, V., & Wang, Y. K. (2021). A digital twin-based decision analysis framework for operation and maintenance of tunnels. Tunnelling and underground space technology, 116, 104125. https://doi.org/10.1016/j.tust.2021.104125
  • Korus, K., Salamak, M., & Winkler, J. (2023, June). Digital Twins as the Next Step in the Design and Management of Bridge Structures. In International Symposium of the International Federation for Structural Concrete (pp. 1586-1593). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-32511-3_162
  • Kaewunruen, S., Sresakoolchai, J., Ma, W., & Phil-Ebosie, O. (2021). Digital twin aided vulnerability assessment and risk-based maintenance planning of bridge infrastructures exposed to extreme conditions. Sustainability, 13(4), 2051. https://doi.org/10.3390/su13042051
  • Bosurgi, G., Celauro, C., Pellegrino, O., Rustica, N., & Giuseppe, S. (2020). The BIM (building information modeling)-based approach for road pavement maintenance. In Proceedings of the 5th International Symposium on Asphalt Pavements & Environment (APE) 5, 480-490. https://doi.org/10.1007/978-3-030-29779-4_47
  • Sairam, N., Nagarajan, S., & Ornitz, S. (2016). Development of mobile mapping system for 3D road asset inventory. Sensors, 16(3), 367. https://doi.org/10.3390/s16030367
  • Schultz, A. J. (2012). The role of GIS in asset management: Integration at the Otay Water Distict. Master’s Thesis, University of Southern California, USA
  • Tao, C. V. (2000). Mobile mapping technology for road network data acquisition. Journal of Geospatial Engineering, 2(2), 1-14.
  • Fleischer, K., & Nagel, H. H. (2001, August). Machine-vision-based detection and tracking of stationary infrastructural objects beside inner-city roads. In ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No. 01TH8585), 525-530. https://doi.org/10.1109/ITSC.2001.948713
  • Schwarz, K. P., & Li, Y. C. (1996). What can airborne gravimetry contribute to geoid determination?. Journal of Geophysical Research: Solid Earth, 101(B8), 17873-17881. https://doi.org/10.1029/96JB00819
  • Murray, S., Haughey, S., Brogan, M., Fitzgerald, C., McLoughlin, S., & Deegan, C. (2011). Mobile mapping system for the automated detection and analysis of road delineation. IET intelligent transport systems, 5(4), 221-230. https://doi.org/10.1049/iet-its.2010.0105
  • Brogan, M., McLoughlin, S., & Deegan, C. (2013). Assessment of stereo camera calibration techniques for a portable mobile mapping system. IET Computer Vision, 7(3), 209-217. https://doi.org/10.1049/iet-cvi.2012.0085
  • Glennie, C. (2007). Rigorous 3D Error Analysis of Kinematic Scanning LIDAR Systems. Journal of Applied Geodesy, 1(3), 147–157. https://doi.org/10.1515/jag.2007.017
  • Toth, C. K. (2009). R&D of Mobile LIDAR Mapping and Future Trends. American Society for Photogrammetry and Remote Sensing Annual Conference, ASPRS, 9-13.
  • Olsen, M. J., Roe, G. V., Glennie, C., Persi, F., Reedy, M., Hurwitz, D., Williams, K., Tuss, H., Squellati, A. & Knodler, M. (2013). Guidelines for the Use of Mobile LIDAR in Transportation Applications, (Vol. 748). Transportation Research Board. https://doi.org/10.13140/RG.2.1.2991.6884
  • Gan-Mor, S., Clark, R. L., & Upchurch, B. L. (2007). Implement lateral position accuracy under RTK-GPS tractor guidance. Computers and Electronics in Agriculture, 59(1-2), 31-38. https://doi.org/10.1016/j.compag.2007.04.008

Year 2023, Volume: 5 Issue: 2, 55 - 66, 15.12.2023

Hüseyin Kurşun

https://doi.org/10.53093/mephoj.1334286

Abstract

References

  • Natsui, R. K., Mireku, K. K., Amuzu, G. G. K., & Sasu, E. (2022, June). An Integrated Geographical Information and Road Asset Management System for road transport network sustainability in developing countries. 28th International Conference on Engineering, Technology and Innovation (ICE/ITMC) & 31st International Association for Management of Technology (IAMOT) Joint Conference (pp. 1-6). IEEE. https://doi.org/10.1109/ICE/ITMC-IAMOT55089.2022.10033144
  • Keleş, M. D., & Aydin, C. C. (2020). Mobil lidar verisi ile kent ölçeğinde cadde bazlı envanter çalışması ve coğrafi sistemleri entegrasyonu-Ankara Örneği. Geomatik, 5(3), 193-200. https://doi.org/10.29128/geomatik.643569
  • Elhashash, M., Albanwan, H., & Qin, R. (2022). A review of mobile mapping systems: From sensors to applications. Sensors, 22(11), 4262. https://doi.org/10.3390/s22114262
  • Luo, H., Wang, C., Wen, C., Cai, Z., Chen, Z., Wang, H., ... & Li, J. (2015). Patch-based semantic labeling of road scene using colorized mobile LiDAR point clouds. IEEE Transactions on Intelligent Transportation Systems, 17(5), 1286-1297. https://doi.org/10.1109/TITS.2015.2499196
  • Yang, B., Dong, Z., Zhao, G., & Dai, W. (2015). Hierarchical extraction of urban objects from mobile laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing, 99, 45-57. https://doi.org/10.1016/j.isprsjprs.2014.10.005
  • Wen, C., Li, J., Luo, H., Yu, Y., Cai, Z., Wang, H., & Wang, C. (2015). Spatial-related traffic sign inspection for inventory purposes using mobile laser scanning data. IEEE Transactions on Intelligent Transportation Systems, 17(1), 27-37. https://doi.org/10.1109/TITS.2015.2418214
  • Wu, Y., Wang, Y., Zhang, S., & Ogai, H. (2020). Deep 3D object detection networks using LiDAR data: A review. IEEE Sensors Journal, 21(2), 1152-1171. https://doi.org/10.1109/JSEN.2020.3020626
  • Broggi, A. (1995, September). A massively parallel approach to real-time vision-based road markings detection. In Proceedings of the Intelligent Vehicles' 95, 84-89. https://doi.org/10.1109/IVS.1995.528262
  • He, Y., Wang, H., & Zhang, B. (2004). Color-based road detection in urban traffic scenes. IEEE Transactions on intelligent transportation systems, 5(4), 309-318. https://doi.org/10.1109/TITS.2004.838221
  • Veit, T., Tarel, J. P., Nicolle, P., & Charbonnier, P. (2008, October). Evaluation of road marking feature extraction. In 2008 11th International IEEE Conference on Intelligent Transportation Systems, 174-181. https://doi.org/10.1109/ITSC.2008.4732564
  • Grejner-Brzezinska, D. A., Li, R., Haala, N., & Toth, C. (2004). From Mobile Mapping to Telegeoinformatics. Photogrammetric Engineering & Remote Sensing, 70(2), 197-210. https://doi.org/10.14358/PERS.70.2.197
  • Chen, S., Chen, F., Liu, J., Wu, J., & Bienkiewicz, B. (2010). Mobile mapping technology of wind velocity data along highway for traffic safety evaluation. Transportation research part C: emerging technologies, 18(4), 507-518. https://doi.org/10.1016/j.trc.2009.10.003
  • Li, R. (1997). Mobile mapping: An emerging technology for spatial data acquisition. Photogrammetric Engineering and Remote Sensing, 63(9), 1085-1092.
  • Poggenhans, F., Pauls, J. H., Janosovits, J., Orf, S., Naumann, M., Kuhnt, F., & Mayr, M. (2018, November). Lanelet2: A high-definition map framework for the future of automated driving. In 2018 21st international conference on intelligent transportation systems (ITSC), 1672-1679. https://doi.org/10.1109/ITSC.2018.8569929
  • Otero, R., Lagüela, S., Garrido, I., & Arias, P. (2020). Mobile indoor mapping technologies: A review. Automation in Construction, 120, 103399. https://doi.org/10.1016/j.autcon.2020.103399
  • Errandonea, I., Beltrán, S., & Arrizabalaga, S. (2020). Digital Twin for maintenance: A literature review. Computers in Industry, 123, 103316. https://doi.org/10.1016/j.compind.2020.103316
  • Yu, G., Wang, Y., Mao, Z., Hu, M., Sugumaran, V., & Wang, Y. K. (2021). A digital twin-based decision analysis framework for operation and maintenance of tunnels. Tunnelling and underground space technology, 116, 104125. https://doi.org/10.1016/j.tust.2021.104125
  • Korus, K., Salamak, M., & Winkler, J. (2023, June). Digital Twins as the Next Step in the Design and Management of Bridge Structures. In International Symposium of the International Federation for Structural Concrete (pp. 1586-1593). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-32511-3_162
  • Kaewunruen, S., Sresakoolchai, J., Ma, W., & Phil-Ebosie, O. (2021). Digital twin aided vulnerability assessment and risk-based maintenance planning of bridge infrastructures exposed to extreme conditions. Sustainability, 13(4), 2051. https://doi.org/10.3390/su13042051
  • Bosurgi, G., Celauro, C., Pellegrino, O., Rustica, N., & Giuseppe, S. (2020). The BIM (building information modeling)-based approach for road pavement maintenance. In Proceedings of the 5th International Symposium on Asphalt Pavements & Environment (APE) 5, 480-490. https://doi.org/10.1007/978-3-030-29779-4_47
  • Sairam, N., Nagarajan, S., & Ornitz, S. (2016). Development of mobile mapping system for 3D road asset inventory. Sensors, 16(3), 367. https://doi.org/10.3390/s16030367
  • Schultz, A. J. (2012). The role of GIS in asset management: Integration at the Otay Water Distict. Master’s Thesis, University of Southern California, USA
  • Tao, C. V. (2000). Mobile mapping technology for road network data acquisition. Journal of Geospatial Engineering, 2(2), 1-14.
  • Fleischer, K., & Nagel, H. H. (2001, August). Machine-vision-based detection and tracking of stationary infrastructural objects beside inner-city roads. In ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No. 01TH8585), 525-530. https://doi.org/10.1109/ITSC.2001.948713
  • Schwarz, K. P., & Li, Y. C. (1996). What can airborne gravimetry contribute to geoid determination?. Journal of Geophysical Research: Solid Earth, 101(B8), 17873-17881. https://doi.org/10.1029/96JB00819
  • Murray, S., Haughey, S., Brogan, M., Fitzgerald, C., McLoughlin, S., & Deegan, C. (2011). Mobile mapping system for the automated detection and analysis of road delineation. IET intelligent transport systems, 5(4), 221-230. https://doi.org/10.1049/iet-its.2010.0105
  • Brogan, M., McLoughlin, S., & Deegan, C. (2013). Assessment of stereo camera calibration techniques for a portable mobile mapping system. IET Computer Vision, 7(3), 209-217. https://doi.org/10.1049/iet-cvi.2012.0085
  • Glennie, C. (2007). Rigorous 3D Error Analysis of Kinematic Scanning LIDAR Systems. Journal of Applied Geodesy, 1(3), 147–157. https://doi.org/10.1515/jag.2007.017
  • Toth, C. K. (2009). R&D of Mobile LIDAR Mapping and Future Trends. American Society for Photogrammetry and Remote Sensing Annual Conference, ASPRS, 9-13.
  • Olsen, M. J., Roe, G. V., Glennie, C., Persi, F., Reedy, M., Hurwitz, D., Williams, K., Tuss, H., Squellati, A. & Knodler, M. (2013). Guidelines for the Use of Mobile LIDAR in Transportation Applications, (Vol. 748). Transportation Research Board. https://doi.org/10.13140/RG.2.1.2991.6884
  • Gan-Mor, S., Clark, R. L., & Upchurch, B. L. (2007). Implement lateral position accuracy under RTK-GPS tractor guidance. Computers and Electronics in Agriculture, 59(1-2), 31-38. https://doi.org/10.1016/j.compag.2007.04.008
There are 31 citations in total.

Details

Primary Language English
Subjects Photogrammetry and Remote Sensing
Journal Section Research Articles
Authors

Hüseyin Kurşun 0000-0002-0342-5210

Early Pub Date October 17, 2023
Publication Date December 15, 2023
Published in Issue Year 2023 Volume: 5 Issue: 2

Cite

APA Kurşun, H. (2023). Accuracy comparison of mobile mapping system for road inventory. Mersin Photogrammetry Journal, 5(2), 55-66. https://doi.org/10.53093/mephoj.1334286
AMA Kurşun H. Accuracy comparison of mobile mapping system for road inventory. MEPHOJ. December 2023;5(2):55-66. doi:10.53093/mephoj.1334286
Chicago Kurşun, Hüseyin. “Accuracy Comparison of Mobile Mapping System for Road Inventory”. Mersin Photogrammetry Journal 5, no. 2 (December 2023): 55-66. https://doi.org/10.53093/mephoj.1334286.
EndNote Kurşun H (December 1, 2023) Accuracy comparison of mobile mapping system for road inventory. Mersin Photogrammetry Journal 5 2 55–66.
IEEE H. Kurşun, “Accuracy comparison of mobile mapping system for road inventory”, MEPHOJ, vol. 5, no. 2, pp. 55–66, 2023, doi: 10.53093/mephoj.1334286.
ISNAD Kurşun, Hüseyin. “Accuracy Comparison of Mobile Mapping System for Road Inventory”. Mersin Photogrammetry Journal 5/2 (December 2023), 55-66. https://doi.org/10.53093/mephoj.1334286.
JAMA Kurşun H. Accuracy comparison of mobile mapping system for road inventory. MEPHOJ. 2023;5:55–66.
MLA Kurşun, Hüseyin. “Accuracy Comparison of Mobile Mapping System for Road Inventory”. Mersin Photogrammetry Journal, vol. 5, no. 2, 2023, pp. 55-66, doi:10.53093/mephoj.1334286.
Vancouver Kurşun H. Accuracy comparison of mobile mapping system for road inventory. MEPHOJ. 2023;5(2):55-66.