Yapay Zeka Hızlı Bakış & Çıkarımlar
AI InsightBu çalışmada üç boyutlu nokta bulutu verisi sağlayan ölçme teknikleri incelenmiş ve nokta bulutu ile gerçekleştirilebilecek kentsel uygulamalar araştırılarak örnek uygulama yapılmıştır.
📌 3 Maddede Bu Makale
Özet
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.
Abstract
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.