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Earn: 1 PDH, 1 AIA LU/Elective; 0.1 IACET CEU

This course may qualify for continuing education through the NYSED. For more information, visit www.op.nysed.gov/prof/pels/peceques.htm.

This course may qualify for continuing education through the FBPE.

Learning Objectives:

  1. Describe what registration is and the different registration methods.
  2. Distinguish between target-based registration and cloud-to-cloud registration.
  3. Identify what survey control is, understand how it’s established, and explain why it’s important.
  4. Determine how best to select a laser scanning registration method for any given project.

BNP Media has met the standards and requirements of the Registered Continuing Education Program. Credit earned on completion of this program will be reported to RCEP.net. Certificates of Completion will be issued to all participants via the RCEP.net online system. As such, it does not include content that may be deemed or construed to be an approval or endorsement by the RCEP.

To receive credit, you are required to read the entire article and pass the quiz. Join POB Premium for complete text and to take the quiz for free. Most states now accept RCEP, IACET, and AIA credits for land surveyors’ requirements. Check your state licensing board for all laws, rules and regulations to confirm.

RCEP and AIA course #161372


Technology has evolved at a rapid pace over the last few decades. One industry that has profited greatly from this technological transition is the construction industry — particularly when it comes to employing the skills of land surveyors and geospatial professionals on construction sites. 

Technological advancements such as mobile apps (and smartphones), autonomous vehicles and heavy equipment, drones/UAVs, robots, augmented reality, and mobile mapping, have helped drive the surveying profession forward, making construction sites safer, data collection and processing more efficient, and businesses more successful. 

One of the most prominent innovations to influence the surveying industry has been 3D laser scanning. Also known as 3D imaging, terrestrial LiDAR, and 3D imaging, laser scanning is becoming a more prominent service in everyday surveying. This is partially due to today’s user-friendly and feature-equipped laser scanners. Using these highly technical products, surveyors can calculate distances from hundreds of feet away, and measure angles, distances, and more without traveling to each individual point. Digitally capturing the shape of physical objects, 3D laser scanning uses a line of laser light to create point clouds of data of an object’s surface.

Although using reality capture solutions such as these can provide ample returns on investment almost immediately, they can also pose challenges in the field, specifically, choosing a registration method that is best suited for the project at hand.

As hardware and software manufacturers continue to develop enhancements in scanning registration and data collection technologies, it is important to understand the benefits and limitations of each method. This course will review topics such as laser scanner accuracy, error propagation, survey control, and more when deciding on a laser scanning registration approach.


What is Registration?

To start, we need to discuss what is laser scanning software, the basics of registration, and why it is important.

As we mentioned earlier, laser scanners deploy a technology called LiDAR (light detection and ranging), which involves collecting measurements using lasers to gauge distances between individual points. Terrestrial laser scanning has been used for various applications, including as-built modeling of architectural and engineering structures, mapping of terrain, vegetation, and other landscape features, and the measurement of movement caused by natural disasters. These scanners are able to record a dense array of distance return values, meaning millions of individualized measurements are made per second, which are then assembled into highly detailed, digital 3D models. These combine into what is commonly referred to as a point cloud.

point cloud

When using terrestrial laser scanning, it’s possible to capture measurements of the environment that are visible from the laser scanners position, otherwise referred to as scanning within line of sight (LOS). Obstructions occur when objects in the environment block LOS from the scanner. To curtail this problem, multiple setups are used to extend LOS and ensure the required level of coverage for the project is achieved. So, in order to gain a complete image, surveyors need multiple scans and, potentially, many different point clouds. All of these scans must then be stitched together with an incredibly high level of precision and accuracy in a process called point cloud registration. Once the registered point cloud is in hand, 3D models and visualizations can be created using that data.

Registration is the first step in point cloud processing and 3D model conception. It is crucial for the overall quality of the final product because registration errors can easily propagate and multiply further in the process.

The registration process is the overarching alignment of scans to each other, based on a reference scan, or to a state or known coordinate system such as a BIM model. This means that the software either relies on the placing of physical ‘registration targets,’ placed in the field being scanned, or by finding patterns in features within a scan. Then, commonalities in scan overlaps have to be identified and aligned with adjacent scans.

The traditional approach to registration requires two steps per point cloud. The first step is determining the correspondences, and the second is estimating the transformation. The correspondences can be geometric measurements like points, lines, planes, and even specific objects. For preparation, surveyors must detect the key points, fit the key lines, places, or extract the specific objects. Then, an extract can match those geometric measurements according to similarities. Alternatively, the geometric or adjacent relationship can be adopted to get correspondences. The second step is transformation estimation. Given the correspondences, the goal is to solve the transformation between two point clouds. This whole process can be achieved through a number of solutions using either one method or a combination. Those methods include: target-based registration, sensor-based registration, and data-driven, or cloud-to-cloud, registration.

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