Light Detection And Ranging or Laser Imaging Detection And Ranging (LiDAR)
Goal(s)
Main objective
LiDAR technology is used to obtain three-dimensional (3D) representations of objects or structures. This representation is a set of many measures. Each measure is applied to a point on a surface. The information recorded depends on the sensor, which may be the coordinates of each point or its reflectivity, among others.
Description
Functioning mode
Laser scanning uses a laser beam to generate a 3D model of the object’s surface to which it is pointing at. This object is represented in the space by its 3D coordinates, which are obtained by computing the distance between the LiDAR system and the target object. This, therefore, classifies remote sensing as an active technology. When the distance measurement device is combined with optomechanical systems or mirrors, which deflect the laser beam and measure the deflection angle, a 3D point cloud is obtained.
The Euclidian coordinates of each point of the cloud are defined by its distance (range) to the sensor and the angles relative to it as in Figure 28.
* Range measurement:
- Time-of-Flight (TOF):
*
- Phase Measurement
*
- Triangulation
* Angle Measurement
Types
To carry out surveys on ground, there are two types of laser scanner systems: Terrestrial Laser Scanner, Mobile Laser Scanner and Aerial Laser Scanner.* Terrestrial Laser Scanner (TLS)
Static scanners, also known as Terrestrial Laser Scanners, were the first type of laser scanners used as surveying for measuring 3D point coordinates. These scanners use high-speed rotating mirrors to deflect the laser beam in the vertical plane, measuring elevation angles. With motors rotating the head around the vertical axis, they can provide measurements at different azimuth angles. Normally, these mirrors can rotate 360º. After performing the survey, a very dense point cloud is obtained in the spherical coordinate system, which will be turned into Cartesian coordinates by most of the scans. The point clouds will be formed by the mentioned coordinates (x, y, z) for each single point, as well as by some other attributes, described later in this document . A representation of this can be seen in Figure 30.
* Mobile Laser Scanner (MLS)
Mobile Laser Scanners are the more appropriate platforms for scanning large infrastructures since TLS required too much time to create their 3D models. Yet, there are two different modes of performing this type of surveys, differing in the imaging procedure, even though the sensors are identical:* Stop-and-go mode (SAG)
At least one laser scanner is placed on a vehicle-borne platform. During the scanning process, the position and orientation of the scanner does not change, remaining static. After every scan, the vehicle changes its position, and the next scan is performed. For each temporary position of the scanner reference point together with the orientation of the scanner axes a new coordinate system is defined. In this way, each point cloud results to be geometrically consistent with the others. With respect to a unique coordinate system there are six geometric degrees of freedom: the three coordinates of the scanner reference point and the three rotation angles of the scanner axes. * On-the-fly mode
The vehicle moves along a predefined trajectory while the laser scanner is continuously working. In order to do that, the Mobile Mapping System (MMS) is formed by the vehicle itself (car) and the laser scanner. Also, the monitoring of the position of the scanning system is possible thanks to the use of a Global Navigation Satellite System (GNSS), combined with an Inertial Measurement System (IMS) and Distance Measurement Indicators (DMI). With this, the so-called trajectory followed by the platform is registered into a global coordinate system. The 3D point cloud is created when the laser scanner is synchronized with the navigation system. In contrast with the previous instruments, in MLS the laser beam is deflected only in one axis, so that the point cloud being recorded is contained in one plane. Thanks to the vehicle motion, a dense 3D point cloud is obtained as the scanned profiles are ranged along the trajectory. Nowadays, most of the systems cover a field of view (FOV) of 360º.
The difference between these two surveying modes, as shown in Figure 31, lies in the registration process. While in the first case, the registration between the different scans performed needs from tie points referenced externally (e.g. GPS), in the second case this is done thanks to the GPS/IMU module already part of the equipment .
Process/event to be detected or monitored
Laser scanner technology is employed to acquire comprehensive data about a real site, namely its layout, shape and other information regarding its general appearance, and use it to produce a virtual representation of the scene. A single scan is discrete and thus limited to a particular time, so in order to record the effects of an event or some process that occurred on the site, several scans must be acquired at different times to be compared.
Physical quantity to be measured
- Geometric information
Laser scanning is used to capture the geometry of the structure. This geometry is represented by 3D Euclidean coordinates that constitute a 3D point cloud. An example of a point cloud is shown in Figure 32.
Figure 32. Point cloud example.
Aside from the 3D geometry obtained with laser scanners, additional physical characteristics, such as those described below, are calculated and assigned to the individual points of the point cloud.
Radiometric information:* Intensity data
Laser scanners are active and remote sensors. They can emit the laser beam at a specific wavelength in the electromagnetic spectrum. Depending on the instrument, green or near infrared wavelengths are used. Recently, systems including several lasers at different wavelengths have been developed to provide multispectral measurements.
The spectral reflectance (intensity data) is the amount of energy reflected by an object’s surface when receiving a laser beam depending on the characteristics of the material. This backscatter generated after the collision is recorded by most LiDAR instruments as a function of time , . Depending on the objects’ surface there might be several function peaks, each of them representing an object measured at a different range.
Also, it is important to highlight that LiDAR intensity values can vary depending on the weather circumstances.* Pulse Returns
The return power is recorded as a function of time after the transmission. The return number is the number of times the emitted laser pulse returns to the LiDAR sensor. The transmission function will be recorded with as many peaks as objects reached by the laser beam, located at different ranges.* Waveform
The LiDAR pulse can be recorded as separate returns (discrete return LiDAR), or as one continuous wave saving the whole return (full-waveform LiDAR). In this way, many objects can be measured from a single emission.
Induced damage to the structure during the measurement
Laser scanning is a non-destructive and non-contact technology. In consequence, it does not induce any damage to the structure.
General characteristics
Measurement type (static or dynamic, local or global, short-term or continuous, etc.)
The captures recorded with laser scanning belong to a given point in time, so they can be described as discrete measurements. Also, the model generated may represent the whole structure or just a given part depending on the method used.
Measurement range
- Time-of-Flight (TOF): Up to several kilometres .
- Phase Measurement: Up to 300 meters .
- Triangulation: Up to 5-10 meters
Measurement accuracy
- Time-of-Flight (TOF): 5 mm
- Phase Measurement: 2-3 mm
- Triangulation: 20μm .
Background (evolution through the years)
LiDAR technology started to grow in the 1960s with several applications in geosciences. Years later, land surveying applications appeared in the picture thanks to the use of airborne profilometers. This equipment resulted to be useful for deriving the vegetation height evaluating the returned signal . During the 1980s and 1990s, the use of laser scanning for environmental and land surveying applications increased. In the second part of the 1990s, civil engineering related applications started to arise, but it was not until the last part of the century and the beginning of the new one when the first terrestrial devices for 3D digitalization performance appeared. From this point, numerous applications for different fields quickly raised, and TLS proved to be the appropriate technology to use when detailed 3D models were required. With the evolution of technology, the resolution and quality of data given by laser scanning devices have been improved. And so, in these final years, mobile mapping systems are arising in order to perform high resolution surveys of large infrastructures (tunnels, roads, urban modelling…) in a short period of time.
Nowadays, the main bottleneck for LiDAR technology is processing the large amounts of data acquired with laser scanning devices. Throughout the years, many tools have been developed for point cloud data processing. Most of them depend on manual or semi-automatic operations that have to be performed by a specialist in the field of geomatics. The challenge now is to develop tools for the efficient automation of data processing, using the information provided by ALS, MLS or TLS devices.
With the emergence of machine learning algorithms, tedious and hard processing tends to disappear. These tools allow not only the development of advanced, efficient and intelligent processing but also interpretation of data. One of the main objectives exploited is obtaining inventories for road, railway or urban management. Now, this has evolved and the trend is to obtain spatial models of infrastructures based on the fusion of geometric and radiometric data and monitoring the infrastructure behaviour and changes through the years. These models can be used as a geometrical basis for BIM (Building Information Modelling) and AIM (Asset Information Model) applications, allowing to have not only as-design representations of the asset but also as-built and as-operate models and update these over time.
An example of a segmented point cloud is shown in Figure 33.
Figure 33. Segura’s roman bridge segmentation (Downstream and Upstream)
Thanks to the information extracted with LiDAR systems, existing deformations in the structures can also be detected and later related with their causing loads in their virtual model. This will help to monitor the structural integrity and the origin of damage. LiDAR data can be used in finite-element analysis, , but the data can also be processed directly. To this end, new algorithms for pathology or damage detection need to be developed. Some of the applications are crack detection ,, general damage identification ,,,,, geometrical variations , and many others to be developed from now and with the evolution of technology.
Although research is continuously updating about automation of data processing, a general and robust methodology is still needed in order to definitely implement LiDAR as a basic tool for civil engineering purposes. As Petrie and Toth (2008) said, laser scanning is probably “the most important geospatial data acquisition technology that has been introduced since the last millennium’’ .
Performance
General points of attention and requirements
Design criteria and requirements for the design of the survey
Survey planning with laser scanner involves: selection of the suitable device (TLS, MLS or ALS); definition of the suitable scanning positions or trajectory; preparation and calibration of the equipment; definition of the working parameters (Maximum range, Pulse repetition frequency…).
Laser scanners are an active remote sensing technology, so surveys can be performed at night. Nevertheless, most of the surveys are planned during the day due to the use of optical cameras along with LS. In any case, it is important to consider the benefits of nightly surveying (less pedestrian and/or roadway traffic, less impact if interrupting the service of any infrastructure…).
A more detailed explanation on how to plan a survey may be consulted following reference.
Procedures for defining layout of the survey
The environment of the survey location must be thoroughly studied prior to the operation to identify the best positions for the equipment (in the case of TLS systems) or trajectory to follow (MLS) in order to register the sections of interest on the infrastructure and other assets that are object of the monitoring activity.
It is important to take into account the vegetation in the survey area. This could be both a good and a bad issue when performing a survey. In some cases, vegetation would provoke occlusions of important areas in the infrastructure, so it would be better to acquire the data in leaf-off conditions. However, in some cases, it would be better to perform the survey during leaf-on campaigns to obtain better ground measurements (ALS).
Low reflectivity surfaces, glass and other transparent or translucent materials can also cause issues on laser detections, such as signal losses or multiple, conflicting signal returns.#
Sensibility of measurements to environmental conditions
Since most of the LiDAR systems work with lasers in the NIR range, dust and vapor can severely affect the results of the measurements. Moreover, some sensors are sensitive to direct sunlight. These factors should be considered when choosing a date for performing the survey, since the weather conditions need to be specific.
Preparation
Procedures for calibration, initialization, and post-installation verification
Calibration procedures are carried out with the objective of adjusting the sensitivity of the sensor at different values.
LiDAR sensors must be calibrated in terms of measured range to the target object and the angle of the rotating mirrors that deflect the laser beam in different directions, which are determined by encoders. The intensity of the returned beam measured by the receiver must be calibrated as well. The manufacturer is usually in charge of carrying out the calibration of this equipment.
The geometric calibration of the offsets measured between the different devices (LiDAR sensors, GNSS antennas, IMU, DMI) must be carried out as well to guarantee a correct point cloud referencing. For such task, a total station can be used.
In the case of MLS scanners, as they work as a component of a Mobile Mapping System, the navigation instruments must be calibrated and initialized as well. For each type of device, the procedures include:*
- GNSS (Global Navigation Satellite Systems, such as GPS, Galileo or GLONASS): the MMS vehicle must initialize the recording of the trajectory. Depending on the navigation system, this must be done either by maintaining a static position for a minimum period of time or moving the vehicle, in both cases in a clear area to avoid obstacles that could interfere with the satellites’ line of sight.
- IMU (Inertial Measurement Unit): the MMS vehicle has to be subjected to sudden movements so the IMU can record accelerations high enough to calibrate the system.
- DMI (Distance Measurement Instrument): to calibrate the device, the odometer is installed on one of the wheels of the vehicle, moving it then along a straight line of a known longitude.
Procedures for estimating the component of measurement uncertainty resulting from calibration of the data acquisition system (calibration uncertainty)
These parameters are determined by the system manufacturer.
Requirements for data acquisition depending on measured physical quantity (e.g. based on the variation rate)
These parameters are determined by the system manufacturer.
A more detailed explanation of these aspects can be found in ,.
Performance
The surveys are taken in successive campaigns, so certain preparations and processing steps must be taken into account to perform the task correctly, and they must be always followed for the measurements to be coherent along the time.* Global reference system (i.e.: Datum, reference ellipsoid, reference geoid for altitude)
- Sensor positions. (TLS case)
- Follow the same trajectory. (MMS case)
- Use same sensor to avoid discrepancies due to the quality of the measurement.
- Make sure weather conditions are adequate in all cases.
- Remove mobile objects that may cause occlusions.
- Repeatability.
Reporting
Surveying campaign report usually include the following sections:* Technical and performance specifications of the employed equipment.
- Description of the different phases/steps of the acquisition process
- Assembling and of the equipment
- Planification of the areas/scenes to be captured and the spots to place the LiDAR (in the case of TLS) or the route to follow (in the case of MLS)
- Marking and measuring of control/support points, consisting of highly visible targets that are individually geo-referenced for later precision assessment and data adjustment.
- Data acquisition, including:
- Initialization and set-up of the equipment carried out prior to the survey.
- Creation of files to store the survey results.
- Data processing and verification:
- For MLS, correction of the trajectory data according to a reference positioning station.
- Point cloud adjustment according to control/support points.
- Results reporting
- For MLS, trajectory calculation precision expressed as RMS error in meters for North, East and Down positions.
- Differences in control/support positions when measured individually and in the point cloud.
- Point density and distribution metrics.
- Conclusions
Lifespan of the technology and required maintenance (if applied for continuous monitoring)
Does not apply.
Interpretation and validation of results
Expected output (Format, e.g. numbers in a .txt file)
Point cloud with the values obtained in the survey. There are different formats, depending on the sensor, and several standards such as .las, .laz or .bin .
Point clouds are organized in such a way that each point has as many attributes as the LiDAR used is able to acquire. Some of these are:* Coordinates of the points expressed in a reference axis.
- Intensity data
- Number of recorded pulse returns and/or waveform
- LiDAR ID that collected the point
- Scanning angle
- Timestamp
Interpretation
- Coordinates: location in the space of the measured point.
- Intensity data: reflectivity of the measured point.
- Pulse returns and waveform: energy intensity belongs to a single beam returned to the sensor over time. Pulse returns are the maximums of this curve energy intensity over time.
- LiDAR ID: ID of the LiDAR that made the measurement of the point.
- Scanning angle: position register by the decoder when the measurement was made.
- Timestamp: time at which the measurement was made.
Validation
Specific methods used for validation of results depending on the technique
Distances between the data and a reference. Visual supervision.
Quantification of the error
The error is quantified by analysing the characteristic points of the point cloud. These points are compared with reference points, quantifying their differences.
Quantitative or qualitative evaluation
Quantitative and qualitative evaluation is applicable.
The qualitative validation consists of human visual supervision of the point cloud recorded. This point cloud is compared with the real environment to check if the data obtained is visually correct.
The quantitative validation requires the usage of a more precise sensor to measure characteristic points of the scene. This allows to have high fidelity measures of those points and use them to quantify the error and make the validation.
Detection accuracy
Changes in the accuracy of point clouds are noticeable regarding the equipment with which they were acquired. Depending on the source of data, the errors registered with laser scanners can vary from 2 mm to 200 cm.
There are three types of errors leading to low accuracy (in general) in laser scanners:* Laser range error: Error when measuring the distance to an object. It is different for every scanner and can be corrected by recalibrating the scanner (by the manufacturer or a specialist in the field).
- Range noise: Measure of the deviation of single readings from the real value within a sample of measurements. It is conditioned by the distance to the object to be scanned and the reflectivity of the object’s material.
- Mechanical error: Difference between the measured and actual horizontal and vertical angles (angular error). It is caused by the mechanical devices forming the laser scanner (mirrors and servos).
The georeferencing devices in charge of the positioning of the 3D data are also a key issue when performing a laser scanning survey. This is supported by GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System) systems that require a specific planning for every measurement campaign. These systems may result in invalid coordinates for the GNSS base station(s), misalignment of the INS with the LiDAR scanner, or a software failure at coordinate conversions. These cause systematic errors that may be identified by sensor calibration, comparing LiDAR with known reference data or additional control operations .
It is important to highlight that accuracy depends not only on the equipment, but also on the geometry of the 3D scenario under study and on the environmental conditions. This affects to the performance of the trajectory and point cloud computation algorithms, and so a specific value of accuracy cannot be given as an answer. The atmospheric condition can also have undesired effects in the LiDAR performance that could lead to noisy measurements.
An example of a MLS is shown in Figure 34.
Advantages
- Big amounts of data (Big Data)
- Short data acquisition time
- High accuracy
- Day and night operation (active sensor)
- Easy integration with other technologies (RGB, Thermography…)
- Access to unreachable areas
- [MLS] Continuous data acquisition
Disadvantages
- Need of experts for surveys’ performing
- Need for large data storage
- Not many automated process
- High processing time
- Sensitive to other reflections (e.g. sunlight)
- [MLS] Reliance on navigation system performance for point referencing
- [TLS] Limited to a single standpoint per acquisition
- Expensive data collection for small areas
- No international protocols
Possibility of automatising the measurements
Currently, it is not possible to automate the survey because an operator is needed to either transport/drive MLS platform or place the TLS at different spots.
Barriers
- Weather conditions must be favourable. It is not possible to make a survey on rainy or foggy days.
- Accessibility. Some places are not accessible for mobile mapping systems, so aerial mapping systems are needed for those cases.
Existing standards
Techniques and Methods 11–B4 .
Applicability
Relevant knowledge fields
- Civil Engineering
- Architectural heritage
- Geosciences
- Archaeology
Performance Indicators
- Cracks.
- Crushing.
- Rupture .
- Spalling.
- Holes.
- Displacement .
- Deformation .
Type of structure
- Bridges
- Buildings
- Façade and annexed elements
- Indoor spaces
- Walls
- Heritage sites
- Roads
- Urban environment
- Street furniture
- Sign posts and markers, traffic lights
- Elements regarding accessibility for people with reduced mobility, .
- Railway network
Spatial scales addressed (whole structure vs specific asset elements)
With laser scanner all the area within the limits established by the maximum range of the system is registered in the point cloud, so the type of technology, either TLS or MLS, the specifications of the system employed (Field-of-View, range, accuracy, etc.) and its location in the site to be captured must be taken into account. Therefore, due planification is necessary to delimit the elements to be included in the scan, which can be either the whole structure or the parts of it that are of interest for the survey. For the case of the monitoring of specific asset elements, if it is not possible to scan them individually, the point cloud can be processed and segmented to isolate the elements of interest and create a new cloud including only them.
Materials
- Stone
- Concrete
- Steel (high difficulty, under development)
- Glass and other transparent/translucent materials
- Wood
- Forests and other vegetation
- Environment in general
Available knowledge
Reference projects
- GIS-Based Infrastructure Management System for Optimized Response to Extreme Events of Terrestrial Transport Networks - SAFEWAY.
* Healthy and Efficient Routes In Massive Open-Data Based Smart Cities: Smart 3D Modelling: HERMES-S3D.
* SITEGI project: Application of Geotechnologies to Infrastructure Management and Inspection.
Other
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