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Digital Solutions in Smart Cities by Using 3D Point Cloud

Yıl 2022, Cilt: 6 Sayı: 2, 208 - 217, 30.12.2022

Öz

Research Problem/Questions – Information extracted from the point cloud is used for robotic vision, mobility, navigation of unmanned vehicles, security, inspection, planning and many more. Three-dimensional modelling is performed by collecting very dense point cloud data with different measurement techniques. Aerial and terrestrial techniques are used in modeling the urban area, land topography and buildings. In-building measurement and modeling techniques are used in the creation of building information systems. Lidar techniques with high measuring speed are used in robotic applications. As can be seen, the selection of the appropriate method for measuring point cloud is very important. Thus, it is necessary to know the point cloud measurement techniques and the applications that can be made with the use of point cloud in smart cities.

Short Literature Review – Mobile mapping systems are used to obtain street views in urban areas [7]. On the other hand, oblique aerial photographs are used in modeling the building facade [8]. The integration of areas that cannot be imaged with these measurement techniques is made with point clouds obtained by terrestrial laser scanning and photogrammetric method [9]. The obtained three-dimensional point cloud data is combined in a common coordinate system, giving a structure that can be questioned and analyzed. As a result of inquiry and analysis, computer aided systems make a decision and perform tasks such as route detection, obstacle detection, mobility, and are considered as smart city applications. The use of 3D point cloud measurement techniques is common in smart cities, especially in the fulfillment of mobility services. The mobility covers services such as robot navigation, transportation with robots, security services, driver assistance systems, unmanned vehicle navigation, traffic safety and crowd management.

Methodology – The main components of smart cities are data, communication infrastructure and software services. Spatial information has an important place in the data infrastructure and is obtained by different techniques. The collection of spatial data is carried out by different techniques. Especially, lidar measurement techniques are widely used in collecting dense point cloud data. Lidar systems are active systems and work with the supported energy. Photogrammetry with passive sensors is another data source. Point cloud surveying techniques, in which active laser and images are used together to provide location data for smart cities. In order for smart city services to be sustainable, spatial information must be up-to-date and renewed periodically. The geometric structure of the environment and other related data are transferred to the computer environment to create a digital twin of the current situation. Since smart applications will be made on this digital data, point cloud measurement techniques have an important place especially in the perception of existing geometry.

Results and Conclusions – Traditional methods based on cameras, radar and thermal sensors in smart cities cannot provide data with sufficient accuracy in all kinds of lighting and weather conditions. Especially for mobile applications, three-dimensional point cloud measurement techniques are widely used. Lidar techniques with low energy requirements and high measurement speed are suitable for mobile mapping, 3D imaging and robotic applications. Photogrammetric point cloud is a low cost measurement technique. Its most important advantage is the ability to create a point cloud from any photograph without the need for technical knowledge. Especially UAV photogrammetry is very suitable for monitoring physical changes in urban areas. Point cloud measurement techniques enable the digitization of smart city services.

Kaynakça

  • [1] M. Garramone, N. Moretti, M. Scaioni, C. Ellul, F. ReCecconi and M.C. Dejaco, “BIM and GIS integration for infrastructıre assest management: A bibliometric analysis,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. VI-4/W1-2020, pp. 77-84, 2020.
  • [2] F. Biljecki, H. Ledoux, J. Stoter and G. Vosselman, “The variants of an LOD of a 3D building model and their influence on spatial analyses,” ISPRS Journal of Photogrammetry and Remote Sensing,, vol. 116, pp. 42-54, 2016.
  • [3] H. Fan and I. Meng,.”Automatic derivation of different levels of detail for 3D buildings modeled by CityGML”. 24th International Cartography Conference, 2016, Santiago, Chile, 15-21 Novenber, pp. 15-21.
  • [4] G.A. Nys, F. Poux, and R. Billen, “CityJSON building generation from airborne LiDAR 3D point clouds,” ISPRS Int. J. Geo-Inf.,.vol. 9, 521, 2020.
  • [5] F. Chiabrando, V. Di Pietra, A. Lingua, Y. Cho and J. “Jeon, An original application of image recognition based location in complex indoor environments.” ISPRS Int. J. Geo-Inf., vol. 6, 56, 2017.
  • [6] C. Wang, S. Hou, C. Wen, Z. Gong, Q. Li, X. Sun and J. Li, “Semantic line framework-based indoor building modeling using backpacked laser scanning point cloud,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 143, pp. 150-166, 2018.
  • [7] S. Nebiker, J. Meyer, S. Blaser, M. Ammann and S. Rhyner, “Outdoor mobile mapping and AI-based 3D object detection with low-cost RGB-D cameras: The use case of on-street parking statistics,” Remote Sens., vol. 13, 3099, 2021.
  • [8] I. Toschi, M.M. Ramos, E. Nocerino, F. Menna, F. Remondino, K. Moe, D. Poli, K. Lrgat and F. Fassi, “Oblique photogrammetry supporting 3D urban reconstruction of complex scenarios.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-1/W1, pp. 519-526, 2017.
  • [9] D. Anton, P. Pineda, B. Medjdoub and A Iranzo, “As-built 3D heritage city modelling to support numerical structural analysis: Application to the assessment of an archaeological remain.” Remote Sens., vol. 11, 1276, 2019.
  • [10] R. Giffinger and H. Gudrun, “Smart cities ranking: an effective instrument for the positioning of the cities?. ACE: Architecture,” City and Environment, vol. 4, No. 12, pp. 7-26, 2010.
  • [11] B. Yang, “Developing a mobile mapping system for 3D GIS and smart city planning.” Sustainability, vol. 11, 3713, 2019.
  • [12] S. Shirowzhan, W. Tan and S.M.E. Sepasgozar, “Digital twin and CyberGIS for improving connectivity and measuring the impact of infrastructure construction planning in smart cities,” ISPRS Int. J. Geo-Inf., vol. 9, 240, 2020.
  • [13] T.H. Kim, C. Ramos and M. Sabah, “Smart City and IoT,” Future Generation Computer Systems, vol. 76, pp. 159-162, 2017.
  • [14] A.S. Syed, D. Sierra-Sosa, A. Kumar and A. Elmaghraby, “IoT in smart cities: A survey of technologies, practices and challenges.” Smart Cities, vol. 4, pp. 429-475, 2021.
  • [15] J.M.L. Domínguez, F. Al-Tam, T.J.M. Sanguino and N. Correia, “Analysis of machine learning techniques applied to sensory detection of vehicles in intelligent crosswalks.” Sensors, vol. 20, 6019, 2020.
  • [16] B. Höfle and M. Hollaus, “Urban vegetation detection using high density full waveform airborne lidar data-combination of object-based image and point cloud analysis.” Int. Arch. Photogramm Remote Sens. Spatial Inf. Sci., vol. XXXVIII-7B, pp. 281–286, 2010.
  • [17] M.H. Stanley and D.F. Laefer, “Metrics for erial, urban LiDAR point clouds,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 175, pp. 168-181, 2021.
  • [18] Leica Geosystems. Available: https://leica-geosystems.com/products/mobile-mapping-systems (Erişim Tarihi: 08.05.2022)
  • [19] C. Altuntas, “Integration of point clouds originated from laser scanner and photogrammetric images for visualization of complex details of historical buildings.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-5/W4, pp. 431-435, 2015.
  • [20] S.M.E. Sepasgozar, P.J. Forsythe and S. Shirowzhan, “Scanners And Photography: A Combined Framework,” 40th Australasian Universities Building Education Association (AUBEA) Conference, 2016. Cairns, Australia , 6–8 July, pp. 819-828.
  • [21] S. Sepasgozar, S. Lim, S. Shirowzhan, P. Kim and Z.M. Nadoushani, “Utilisation of a new terrestrial scanner for reconstruction of as-built models: A comparative study,” International Symposium on Automation and Robotics in Construction, 2015, Oulu, Finland, 15-18 June, pp. 1-9.
  • [22] H. Lahamy and D.D. Lichti, “Towards real-time and rotation-invariant American sign language alphabet recognition using a range camera.” Sensors, vol. 12, no. 11, pp. 14416-14441, 2012.
  • [23] M. Yekkehfallah, M. Yang, Z. Cai, L. Li and C. Wang, “Accurate 3D localization using RGB-TOF camera and IMU for industrial mobile robots,” Robotica, vol. 39, no. 10, pp. 1816-1833, 2021.
  • [24] J. Wülfing, J. Hertzberg, K. Lingemann, A. Nüchter, T. Wiemann, and S. Stiene, “Towards real time robot 6D localization in a polygonal indoor map based on 3D ToF camera data.” IFAC Proceedings, vol. 43, No. 16, pp. 91-96, 2010.
  • [25] L. Cao, H. Liu, X. Fu, Z. Zhang, X. Shen and H. Ruan, “Comparison of UAV LiDAR and digital aerial photogrammetry point clouds for estimating forest structural attributes in subtropical planted forests,” Forests, vol. 10, 145, 2019.
  • [26] N. Haala, M. Rothermel and S. Cavegn, “Extracting 3D urban models from oblique aerial images, Joint Urban Remote Sensing Event (JURSE),” IEEE Xplore, vol. 15201729, pp. 1-4, 2015.
  • [27] R. Niederheiser, M. Winkler, V. Di Cecco, et. al., “Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain.” GIScience & Remote Sensing, Vol. 58, No. 1, pp. 120-137, 2021.
  • [28] Velodyne Lidar, Available: https://velodynelidar.com/products/velarray-m1600/ (Erişim Tarihi: 08.05.2022)
  • [29] Quanergy, Available: https://quanergy.com/applications/smart-city/ (Erişim Tarihi: 06.05.2022) [30] Bosch, Available: https://www.bosch.com.tr/ueruen-ve-hizmetlerimiz/akilli-sehirler/ (Erişim Tarihi: 06.05.2022)
  • [31] Hitachi, Available: https://social-innovation.hitachi/en-us/think-ahead/transportation/?WT.ac=si_us_sp_thiah_tran (Erişim Tarihi: 10.05.2022)
  • [32] Paulus, S., 2019. Measuring crops in 3D: using geometry for plant phenotyping, Plant Methods, 15, 103.
  • [33] Ouster, Available: https://ouster.com/products/scanning-lidar/os1-sensor/ (Erişim Tarihi: 21.11.2022).

Akıllı Şehirlerde 3B Nokta Bulutu ile Digital Çözümler

Yıl 2022, Cilt: 6 Sayı: 2, 208 - 217, 30.12.2022

Öz

Akıllı şehir, mevcut kaynaklar ve altyapı olanakları ile ihtiyaçların etkili, verimli ve sürdürülebilir bir şekilde karşılanması için güncel teknolojinin en yüksek seviyede kullanıldığı sistemler bütünüdür. Akıllı şehirlerin başlıca bileşenleri; veri, iletişim alt yapısı ve yazılım hizmetleridir. Akıllı şehirlerde çok sayıda karar ve eylem konum analizine dayalı olarak gerçekleştirilir. Bu nedenle üç boyutlu ölçme teknikleri akıllı şehir uygulamalarının vazgeçilmez bir bileşeni ve veri kaynağıdır. Nokta bulutu ölçme verisinden cisimlerin tanımlaması, boyutlandırması, takibi yapılabilir, değişimi izlenebilir, ayrıca hareketli cisimlerin hızı ve hareket doğrultuları belirlenebilir. Nokta bulutundan çıkarılan bilgiler robotik görme, mobilite, insansız araçların navigasyonu, güvenlik, denetim, planlama ve daha pek çok amaçla kullanılmaktadır. Farklı ölçme teknikleri ile çok yoğun nokta bulutu verisi toplanarak ölçme alanı üç boyutlu modellenebilmektedir. Kentsel alan, arazi topoğrafyası ve binaların modellenmesinde hava ve yersel ölçme teknikleri kullanılmaktadır. Bina bilgi sistemlerinin oluşturulmasında bina içi ölçme ve modelleme teknikleri kullanılır. Robotik uygulamalarda ölçme hızı yüksek Lidar teknikleri kullanılmaktadır. Görüldüğü gibi amaca uygun nokta bulutu ölçme yöntemi seçimi çok önemlidir. Bu çalışmada üç boyutlu nokta bulutu verisi sağlayan ölçme teknikleri incelenmiş ve nokta bulutu ile gerçekleştirilebilecek kentsel uygulamalar araştırılarak örnek uygulama yapılmıştır.

Kaynakça

  • [1] M. Garramone, N. Moretti, M. Scaioni, C. Ellul, F. ReCecconi and M.C. Dejaco, “BIM and GIS integration for infrastructıre assest management: A bibliometric analysis,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. VI-4/W1-2020, pp. 77-84, 2020.
  • [2] F. Biljecki, H. Ledoux, J. Stoter and G. Vosselman, “The variants of an LOD of a 3D building model and their influence on spatial analyses,” ISPRS Journal of Photogrammetry and Remote Sensing,, vol. 116, pp. 42-54, 2016.
  • [3] H. Fan and I. Meng,.”Automatic derivation of different levels of detail for 3D buildings modeled by CityGML”. 24th International Cartography Conference, 2016, Santiago, Chile, 15-21 Novenber, pp. 15-21.
  • [4] G.A. Nys, F. Poux, and R. Billen, “CityJSON building generation from airborne LiDAR 3D point clouds,” ISPRS Int. J. Geo-Inf.,.vol. 9, 521, 2020.
  • [5] F. Chiabrando, V. Di Pietra, A. Lingua, Y. Cho and J. “Jeon, An original application of image recognition based location in complex indoor environments.” ISPRS Int. J. Geo-Inf., vol. 6, 56, 2017.
  • [6] C. Wang, S. Hou, C. Wen, Z. Gong, Q. Li, X. Sun and J. Li, “Semantic line framework-based indoor building modeling using backpacked laser scanning point cloud,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 143, pp. 150-166, 2018.
  • [7] S. Nebiker, J. Meyer, S. Blaser, M. Ammann and S. Rhyner, “Outdoor mobile mapping and AI-based 3D object detection with low-cost RGB-D cameras: The use case of on-street parking statistics,” Remote Sens., vol. 13, 3099, 2021.
  • [8] I. Toschi, M.M. Ramos, E. Nocerino, F. Menna, F. Remondino, K. Moe, D. Poli, K. Lrgat and F. Fassi, “Oblique photogrammetry supporting 3D urban reconstruction of complex scenarios.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-1/W1, pp. 519-526, 2017.
  • [9] D. Anton, P. Pineda, B. Medjdoub and A Iranzo, “As-built 3D heritage city modelling to support numerical structural analysis: Application to the assessment of an archaeological remain.” Remote Sens., vol. 11, 1276, 2019.
  • [10] R. Giffinger and H. Gudrun, “Smart cities ranking: an effective instrument for the positioning of the cities?. ACE: Architecture,” City and Environment, vol. 4, No. 12, pp. 7-26, 2010.
  • [11] B. Yang, “Developing a mobile mapping system for 3D GIS and smart city planning.” Sustainability, vol. 11, 3713, 2019.
  • [12] S. Shirowzhan, W. Tan and S.M.E. Sepasgozar, “Digital twin and CyberGIS for improving connectivity and measuring the impact of infrastructure construction planning in smart cities,” ISPRS Int. J. Geo-Inf., vol. 9, 240, 2020.
  • [13] T.H. Kim, C. Ramos and M. Sabah, “Smart City and IoT,” Future Generation Computer Systems, vol. 76, pp. 159-162, 2017.
  • [14] A.S. Syed, D. Sierra-Sosa, A. Kumar and A. Elmaghraby, “IoT in smart cities: A survey of technologies, practices and challenges.” Smart Cities, vol. 4, pp. 429-475, 2021.
  • [15] J.M.L. Domínguez, F. Al-Tam, T.J.M. Sanguino and N. Correia, “Analysis of machine learning techniques applied to sensory detection of vehicles in intelligent crosswalks.” Sensors, vol. 20, 6019, 2020.
  • [16] B. Höfle and M. Hollaus, “Urban vegetation detection using high density full waveform airborne lidar data-combination of object-based image and point cloud analysis.” Int. Arch. Photogramm Remote Sens. Spatial Inf. Sci., vol. XXXVIII-7B, pp. 281–286, 2010.
  • [17] M.H. Stanley and D.F. Laefer, “Metrics for erial, urban LiDAR point clouds,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 175, pp. 168-181, 2021.
  • [18] Leica Geosystems. Available: https://leica-geosystems.com/products/mobile-mapping-systems (Erişim Tarihi: 08.05.2022)
  • [19] C. Altuntas, “Integration of point clouds originated from laser scanner and photogrammetric images for visualization of complex details of historical buildings.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-5/W4, pp. 431-435, 2015.
  • [20] S.M.E. Sepasgozar, P.J. Forsythe and S. Shirowzhan, “Scanners And Photography: A Combined Framework,” 40th Australasian Universities Building Education Association (AUBEA) Conference, 2016. Cairns, Australia , 6–8 July, pp. 819-828.
  • [21] S. Sepasgozar, S. Lim, S. Shirowzhan, P. Kim and Z.M. Nadoushani, “Utilisation of a new terrestrial scanner for reconstruction of as-built models: A comparative study,” International Symposium on Automation and Robotics in Construction, 2015, Oulu, Finland, 15-18 June, pp. 1-9.
  • [22] H. Lahamy and D.D. Lichti, “Towards real-time and rotation-invariant American sign language alphabet recognition using a range camera.” Sensors, vol. 12, no. 11, pp. 14416-14441, 2012.
  • [23] M. Yekkehfallah, M. Yang, Z. Cai, L. Li and C. Wang, “Accurate 3D localization using RGB-TOF camera and IMU for industrial mobile robots,” Robotica, vol. 39, no. 10, pp. 1816-1833, 2021.
  • [24] J. Wülfing, J. Hertzberg, K. Lingemann, A. Nüchter, T. Wiemann, and S. Stiene, “Towards real time robot 6D localization in a polygonal indoor map based on 3D ToF camera data.” IFAC Proceedings, vol. 43, No. 16, pp. 91-96, 2010.
  • [25] L. Cao, H. Liu, X. Fu, Z. Zhang, X. Shen and H. Ruan, “Comparison of UAV LiDAR and digital aerial photogrammetry point clouds for estimating forest structural attributes in subtropical planted forests,” Forests, vol. 10, 145, 2019.
  • [26] N. Haala, M. Rothermel and S. Cavegn, “Extracting 3D urban models from oblique aerial images, Joint Urban Remote Sensing Event (JURSE),” IEEE Xplore, vol. 15201729, pp. 1-4, 2015.
  • [27] R. Niederheiser, M. Winkler, V. Di Cecco, et. al., “Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain.” GIScience & Remote Sensing, Vol. 58, No. 1, pp. 120-137, 2021.
  • [28] Velodyne Lidar, Available: https://velodynelidar.com/products/velarray-m1600/ (Erişim Tarihi: 08.05.2022)
  • [29] Quanergy, Available: https://quanergy.com/applications/smart-city/ (Erişim Tarihi: 06.05.2022) [30] Bosch, Available: https://www.bosch.com.tr/ueruen-ve-hizmetlerimiz/akilli-sehirler/ (Erişim Tarihi: 06.05.2022)
  • [31] Hitachi, Available: https://social-innovation.hitachi/en-us/think-ahead/transportation/?WT.ac=si_us_sp_thiah_tran (Erişim Tarihi: 10.05.2022)
  • [32] Paulus, S., 2019. Measuring crops in 3D: using geometry for plant phenotyping, Plant Methods, 15, 103.
  • [33] Ouster, Available: https://ouster.com/products/scanning-lidar/os1-sensor/ (Erişim Tarihi: 21.11.2022).
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Cihan Altuntaş 0000-0002-5754-2068

Yayımlanma Tarihi 30 Aralık 2022
Gönderilme Tarihi 23 Kasım 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 6 Sayı: 2

Kaynak Göster

IEEE C. Altuntaş, “Akıllı Şehirlerde 3B Nokta Bulutu ile Digital Çözümler”, IJMSIT, c. 6, sy. 2, ss. 208–217, 2022.