What Is a Point Cloud?
Point Cloud, or the three-dimensional coordinate points, represent the features of a terrain projected in the three-dimensional coordinate system of a computer. These coordinates of the objects on each pixel are calculated using Computer Vision. Each point possesses its own coordinates, and when these points are displayed together, they form a cluster that reflects the representation of the terrain as it appears on the image. These Point Clouds can be utilized to calculate various volumes, distances, heights, and areas as well.
Understanding Point Cloud Processing
1. Processing Raw Data
Most raw data is recorded in a proprietary format that is specific to the manufacturer or brand. This necessitates the use of brand-specific software to convert these data into a Point Cloud that can be used with other software. The raw data may include images if the sensor is equipped with an integrated color camera.
Initial Outcome - Point Cloud
Once the processing has transformed the electrical wave data into three-dimensional coordinate values, the initial outcome is a Point Cloud. It records the intensity of the reflection (if the sensor supports multiple reflection values), the strength of that reflection, and the three-dimensional coordinates of each point. It also includes the color of each point if the sensor is equipped with an integrated camera or supports the colorization of the point cloud.
2. Refining the Accuracy of the Point Cloud
To enhance the precision and accuracy of the Point Cloud in terms of its spatial positioning, further adjustments can be made. This can be achieved through various methods such as Boresight Calibration, Strip Alignment, Smoothing Points, Outlier Filtering, and Vertical Adjustment.
Boresight Calibration
Boresight Calibration is the process of correcting positional discrepancies caused by shifts or movements between the GNSS coordinates on the drone, the three-dimensional coordinates of the IMU, and the shift between the positions of the IMU and the Sensor. This correction results in a recalculation of the coordinates of the Point Cloud, enhancing the positional accuracy of the points, particularly the horizontal coordinates.
Strip Alignment / Strip Adjustment
Strip Adjustment ensures that the Point Clouds obtained from scanning on two adjacent flight lines fit together more closely. This remediation helps reduce discrepancies between the data from the two flight lines, making the collected information more true to reality. Consequently, this adjustment enhances the overall precision of the Point Cloud.
Vertical Adjustment
Vertical Adjustment is the process of correcting elevation values. Field data can be collected and used to adjust elevations to conform to a desired height system. The adjustment only affects the vertical values and does not alter the horizontal coordinates.
Noise Removal and Smoothing
The noise removal technique is a data processing step intended to denoise and smooth the Point Cloud. This adjustment enhances the realism and smoothness of the Point Cloud by eliminating erroneous data, thereby ensuring that subsequent calculations and interpretations align closely with the true conditions of the area.
3. Point Cloud Classification
After the accuracy of the data has been adjusted, Point Cloud classification comes into play to facilitate easier differentiation and identification of objects when creating maps and models. This involves distinguishing between objects and the earth's surface to achieve greater clarity in the representations.
Final Results Obtained
After enhancing the accuracy and precision of the Point Cloud, we can proceed to create the Digital Surface Model (DSM) or classify the Point Cloud to sort different types. Subsequently, we take these categorized Point Clouds to develop detailed elevation models specific to particular needs, such as the Digital Terrain Model, or to calculate the volume or height of trees, among other applications.
LiDAR VS. Photogrammetry
Photogrammetry is the science of making measurements from photographs. This method uses software to process hundreds of images, which are combined to calculate the coordinates of each pixel on Earth's surface. As a result, these coordinates form a three-dimensional Point Cloud that represents the topography or various objects captured within the images. The photography process requires a significant amount of overlap known as Overlap-Sidelap. The greater the overlap, the more accurate the calculated coordinates.
Following this, the images obtained are stitched together and adjusted to correct any positional distortions, ultimately creating one large, flat image map known as an Orthomosaic map. Used extensively in surveying and mapping work, the Point Cloud can also be employed to generate a Digital Surface Model (DSM), a Digital Terrain Model (DTM), and Contour Lines, which are instrumental for construction and design purposes.
DJI Zenmuse L2
The DJI Zenmuse L2 is a cutting-edge LiDAR sensor for drones, offering unmatched precision in data collection. It enables operators to capture detailed 3D models of terrain and objects with exceptional accuracy, serving industries like construction, forestry, and infrastructure inspection. With the ability to penetrate obstacles and provide up to 5 returns, the DJI Zenmuse L2 is revolutionizing data collection and analysis, making it a vital tool for mapping professionals.
In conclusion, understanding the process of Point Cloud processing is essential for creating accurate and detailed representations of terrains and objects. From converting raw data to refining accuracy and classifying Point Clouds, each step plays a crucial role in generating valuable insights for various applications. Whether utilizing LiDAR or photogrammetry, the end results offer a wealth of information for mapping, modeling, and analysis purposes. By delving deeper into the world of Point Clouds, you can unlock a plethora of possibilities in fields such as surveying, construction, and design. So, dive into this fascinating technology and explore the endless opportunities it presents for innovation and discovery.
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