A Japanese research team recently published a paper outlining an innovative approach to surveying using Unmanned Aerial Vehicles (UAVs), commonly known as drones. The drones collect topographic and architectural data, which are used to create 3D computer-generated images. The researchers aim to enhance the method’s accuracy by fine-tuning the coordination of hardware and data. This would allow a more accurate assessment of construction sites and disaster areas. The paper was published in English in the Journal of Digital Life, an interdisciplinary online journal focused on digital technology.
On September 17, Nagasaki Prefecture made 3D data of nearly the entire prefecture available for free on its website, Open Nagasaki. This data is open for both commercial and non-commercial purposes. Open Nagasaki presents a grid map of the prefecture, allowing users to download 3D data for the area they select. For instance, users can download the 3D data for Minamiyamate-cho in Nagasaki City, home to the Former Glover House, a World Heritage Site. They can view the 3D model from various angles and observe how the area gently slopes down toward the inland sea. This feature is not easily discernible from 2D aerial photographs. The essential component in generating this 3D data is UAVs equipped with laser scanning units. These UAVs collect an extensive set of “point” data, complete with location and color information, by projecting lasers from the sky to the earth’s surface. The amalgamation of this data, 3D point cloud data, accurately replicates real-world topography and urban landscapes.
However, ensuring the accuracy of the 3D point cloud data is vital, and researchers acknowledge that measurement errors are inevitable. In a 2018 study, Nakamura and his team identified three primary issues with previous measurement techniques. The first problem lies in the decline in measurement precision when the UAV’s flight speed is inconsistent. The UAV’s flight pattern resembles painting a canvas with a brush, which means numerous errors can occur in data collection during acceleration and deceleration. The second issue is associated with using data from satellite positioning systems (GNSS) like GPS without adjustments, leading to discrepancies in the directions of elevation. The third challenge involves the degradation of point cloud data precision caused by the inclusion of data from distant objects measured at unsuitable laser incidence angles. For instance, when different routes are used to measure the same object, the data from closer and farther positions become mixed. To address these problems, the research team first reviewed the sensors and devices mounted on the UAV. This reduced the slight time disparities between devices and improved elevation measurement accuracy.
To resolve the issue of varying UAV flight speeds, highly accurate travel data was extracted from the GNSS receiver to determine flight conditions. This allowed data collected during acceleration and deceleration to be excluded. The 3D images were generated using only the data collected while the UAV flew in a straight light at a consistent speed, preserving accuracy. The problem of deviations in elevation direction was tackled by establishing a “reference point for adjustment” at a flat location with nothing obstructing the sky. Then, the deviation of the point cloud data was adjusted in the direction of elevation. For the issue of data measured by multiple routes for the same object, the team attempted to eliminate inaccurate data by dividing the entire point cloud data into specific-sized grids. Then, they extracted the data measured from the nearest location for each grid.
