Accuracy, precision, relative accuracy, absolute accuracy are terms that are commonly used in the mobile mapping industry. Here is a refresh and the different ways by which Viametris technology can help you obtain the most accurate results possible.

Definitions

The accuracy of a point cloud is the difference (also called standard deviation) between the size of a real-world object and its dimensions obtained from a measurement in the point cloud. A relative accuracy of 1 cm means that a real-world object of X meters can be expected to be measured at X + or – 1 cm. If an operator make a BMS3D scan of a street with a bench of 63 cm high, he or she can expect to measure the bench in the point cloud with a dimension ranging from 62 to 64 cm. The accuracy depends on several parameters:

  • the range at which the object is captured
  • the intrinsic quality of the LiDAR scanner (also called measurement noise)
  • the calibration of the LiDAR scanner
  • environmental parameters (reflectivity of the object, weather, etc.)

Precision is the ability to reliably repeat the same measurement over and over. In the first example, if the bench height was measured at a value of 63 cm in the point cloud, a 100% precise mobile scanning system would produce a measurement of 63 cm during a second scan, a third scan, a fourth scan, etc. Repeating the scan a thousand times would yield the same measurement in the point cloud, whether this measurement is correct (accurate) or biased (un-accurate).

Absolute accuracy applies to scans that are geo-referenced through the use of GNSS sensors. They have coordinates expressed in a specific referential (also called projection) which can be chosen according to the project specifications. GNSS satellite technology allows us to navigate the world on a daily basis with embedded GNSS sensors and mobile phone applications. The accuracy is then around 1 meter. The GNSS antennas and receivers in Viametris mobile mapping scanners are PPK based and allow to obtain a position on the planet within a few centimeters (typically 2-3 cm in good GNSS reception environments) thanks to differential GNSS corrections provided by base stations (EUREF grid for instance). Surveying applications usually require this 2-3 cm (X, Y, Z) absolute accuracy for final deliverables.

Measurement noise

When dealing with 3D scanning another parameter to consider is the measurement noise.

There are two types of measurements devices: passive and active devices. A ruler to measure a line on a piece of paper is passive. It does not produce a signal to generate its measurement. Therefore the measurement noise is low or non-existent. 3D LiDAR scanners are active measurement devices. They produce energy to send out a signal and receive it. The received signal is the measured value. Therefore, like any electronic instrument, LiDAR scanners accounts for a measurement noise parameter.

With no need to go in further technical details, one can simply remember that a physical system is never perfect, and therefore its level of quality differs. For a LiDAR scanner, the quality (among other parameters like signal intensity, laser frequency, etc.) is defined by the measurement noise and is dependent on the fineness with which the optical system (lasers emitting and receiving pulses) has been manufactured. Usually, the more expensive the LiDAR, the more carefulness is taken during the manufacturing process and thus the lower the measurement noise of the component.

Interpretation in a point cloud

When measuring an object in a point cloud, there will always exist a measurement noise. An example is the measurement of a wall width. If scanned from only one side, a wall should theoretically be a perfect plane which width should not be wider than the size of a reflected laser pulse (1-2 millimeters). However, in reality one will obtain a cluster of points giving a width of a few millimeters or a few centimeters (depending on the range, type of LiDAR, environmental conditions, etc.). This phenomenon is also relevant for static scanners on a lower scale.

Example of noise measurement

Solutions

To counterbalance the issue raised by measurement noise, at Viametris we recalibrate every single LiDAR scanner that are used in our systems when received from the manufacturer. Our method has been refined for +10 years of R&D and ensure the maximum reduction of noise. The results are significant and especially visible at long ranges (top of buildings, etc.). Overall, this contributes to improve the relative accuracy of the point clouds that our mobile mapping scanners deliver.

Another method that we use to improve the relative accuracy of our point clouds is to carefully select the best LiDAR scanners available on the market to deliver accurate and cost-efficient solutions for each application.

Before filtering

The last option we use to help CAD and BIM drawers in their modeling is the use of filtering. With our filtering algorithms, we can remove the uncertain measured points and smooth the data. Re-using the wall example, this means that intelligent algorithms will remove all the points that are too distant the cluster of points the most representative of the wall.

After filtering

All these three steps contribute to improve the relative accuracy of the scan. It also helps drawers and BIM modelers to create their deliverables.

Conclusion

Now that we have understood all the different parameters that can influence the accuracy of our measurements, one can understand that reaching very high level of accuracies for end deliverables (2D plans, BIM models, etc.) is a complex task. Thanks to its expertise on hardware sensors and proprietary algorithms, Viametris technology is the best way to provide you with the most accurate point clouds.

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