9.0 BACKGROUND

9.1      Typical components of mobile LIDAR systems

Mobile LIDAR Systems (MLS) can be configured in a variety of ways. A precise time stamp is used to synchronize the measurements from all system components to a common time reference frame.   Components (Figure 5) include:

Figure 5:  Components of a Mobile LIDAR system.

Laser Scanners

Laser scanners fire pulses or emit continuous waves at fixed angular increments to determine the range to objects. Hence, native scan data consists of angles and ranges with timestamps. Although numerous commercially produced laser scanners are available for mobile LIDAR systems, there are two basic modes of operation:

1. Some use a static terrestrial LIDAR unit that has been set to operate in a line scan mode. In this mode the scan head remains fixed, and only internal mirror movement takes place. In order to collect a full 360° range of points multiple scanners are typically added to the system.
2. Others have a rotating scan head (often tilted) with fixed laser(s) collecting data in a 360° planar sweep.

In either case, the movement of the vehicle coupled with the scanning plane of the sensor enables the system to collect data points across a wide window. Further, geometric orientation (i.e., look angle, distance to target) of the scanning heads relative to the surface of interest (e.g., horizontal ground surface vs. vertical building facades) plays a pivotal role in overall data quality because the incidence angle at which the laser strikes the surface causes variations in ranging accuracy.

Scanners also provide an intensity (return signal strength), which is an indication of target reflectivity and can be helpful to distinguish objects in the point cloud. However, intensity values vary by system characteristics, scanning geometry, multiple returns (e.g. the light\energy is split between multiple objects) and material type. Normalization procedures are being refined to correct for system characteristics and scanning geometry to enable consistent results between acquisitions, but this is still in the research and development stage.  Hence, intensity values are useful in distinguishing between features within a dataset but should not be interpreted as absolute values and compared between datasets.  Intensity measurements should be provided with the point cloud as a standard deliverable.  However, there are no established quality control procedures in place to ensure the accuracy of intensity values.

Recommendation:  Request intensity values to be provided with scan data so you can use that information for visualization purposes to identify relative differences between objects in the point cloud.

Global Navigation Satellite System (GNSS) Receivers

The Global Navigation Satellite System (GNSS) is an expansion of the U.S. Global Positioning System (GPS) to include the Russian Glonass system, European Galileo (future), and Chinese Compass (future) satellite positioning systems. GNSS receivers provide three primary observations to the MLS:  time, position, and velocity (speed and direction) measurements. Position and velocity information is provided to the logging computer(s) and also to the Inertial Measurement Unit (IMU, described below). An accurate GNSS receiver is vital to precisely geo-referencing the MLS point cloud, particularly over large distances. While real-time kinematic (RTK) GNSS processing (i.e., data are corrected for GNSS errors in real-time) is a possibility for MLS, data are often handled using post-processed kinematic (PPK) techniques to provide more flexibility during acquisition, and more reliability for final trajectory estimates. In either case, a base station needs to be close (within 5-10 miles) to the MLS for best results.

Significant pre-planning should be conducted to ensure that site conditions are appropriate for GNSS data acquisition.  Guidelines for GNSS planning are frequently available as part of existing DOT and other transportation agency standards and are not elaborated herein.  Key components of this preplanning stage include:

• Checking the satellite almanac for good geometry based on site-specific obstructions and satellite positions.  (Most vendors provide software for this).  Note that these checks should be performed at multiple sites across the project area if substantial time will elapse during acquisition (more than a few hours), the site spans several miles or more, or obstruction geometry varies significantly across the site.
• In some instances line of site obstructions to the satellite, and multipath from buildings and trees may be unavoidable, resulting in a significant degradation in the GPS data quality (positional dilution of precision, PDOP, recommended to be less than 5 for highest quality data), which will produce more reliance on the IMU as the primary relative positioning tool.
• Awareness of atmospheric activity,
• Keeping baseline lengths to a minimum (<5 to 10 miles for highest quality data).

Inertial Measurement Units (IMU)

The IMU performs two key functions: first, it provides orientation or attitude information (i.e., the roll, pitch and heading of vehicle), and second, it assists in position estimation, particularly when GNSS quality degrades. The GNSS system typically reports positioning information at rates of 1-10 Hz (i.e., one to ten measurements per second) while the IMU typically reports orientation information at a rate of 100-2000 Hz. The denser sampling by the IMU becomes increasingly important as the speed of the vehicle increases (e.g., a vehicle traveling at 60 MPH (97 km/h) will travel 88 ft. (27 m) in one second).

As GNSS positioning degrades, the IMU will begin to manage more of the positioning/orientation information using a filtering scheme (e.g., Kalman filter), which optimally combines all measurements of vehicle motion to minimize geo-location errors. Depending on the accuracy of the IMU (i.e., drift rate), the IMU may maintain accurate point cloud geo-referencing without the aid of GNSS positioning over extended periods of time.  See Section 10.2 for current IMU capabilities.

Distance Measurement Indicators (DMI)

A DMI is an encoder, normally placed on one of the wheels of the MLS vehicle, and measures tire rotation, which indirectly gives an estimate of distance travelled. A DMI is used in some MLS systems and serves to supplement GNSS and IMU with additional relative positioning information. The DMI is also incorporated into the Kalman filtering scheme in order to provide forward velocity information for calculating the trajectory. The DMI may also be used as the primary triggering device for image capture points based on the distance moved along the ground surface.

Digital Cameras

Points collected by the laser scanners are generally converted to coordinates (i.e., X, Y, and Z) and usually contain a LIDAR intensity measure. To aid in visualization, digital cameras are often incorporated into the MLS so that each individually scanned point can be colored by a red, green, blue (RGB) value depicting that color in the real world. Different MLS will have varying camera arrangements ranging from front, rear, or side cameras to 360 degree panoramic cameras.   Many systems also acquire imagery as a video stream, similar to video logging equipment.

This additional color information provides a greater level of detail, which can be exploited for advanced point cloud processing techniques such as automated sign extraction based on color. Further, geo-referenced images mapped to the point cloud can enable users to create line work and annotations directly on the images that are linked to the point cloud rather than having to directly interface with the point cloud.

However, there are important considerations when working with images rather than the point cloud. First, although the cameras are normally calibrated, there will still be parallax, which will lead to slight offsets between the point clouds and the images. The impact of parallax will be larger closer to the scanner and minimized further away. Generally, fits can be obtained to limit these offsets to within a few pixels.  Second, photography is a passive sensing technology. This means that the quality of the image will vary depending on exposure, focus of the camera, and lighting conditions for the imaged scene.

Recommendation:  Request co-acquired imagery to be delivered with your LIDAR data and geo-referenced to the point cloud.

Recommendation:  Be sure that the data provider understands your plans for using the photographic information to ensure that they provide imagery taken from the appropriate viewpoint and with proper lighting conditions.

Rigid Platform

The rigid platform is the device that firmly attaches the laser scanners, GNSS receivers, IMU’s, digital cameras, and any ancillary devices into one cohesive unit. Each component of the platform needs to be carefully calibrated so that the offsets between each component are well known and remain stable. The platform also permits the MLS to be transferred from vehicle-to-vehicle with much more ease than moving individual components.

Other Ancillary Devices

Many other devices may be added to a MLS in order to provide additional value to the end user. Audio and video recording may be utilized for operators to make oral or visual notes as needed during data acquisition. A computing system must be incorporated to log the very large amounts of data acquired and to provide a user interface to command and control the MLS. The addition of many electrical components may exceed the electrical power output of the vehicle used for the MLS; higher output alternators and extra batteries must often be installed to provide the additional and redundant power sources.

9.2      Comparison to airborne LIDAR systems

Often, projects will require a combination of airborne (both fixed wing and low-flying helicopter), mobile, and static LIDAR acquisition.  This section will discuss key differences and similarities between airborne and mobile LIDAR data sources (Figure 6).

Key differences between mobile LIDAR (MLS) and airborne LIDAR (ALS) systems include:

• Airborne scanning is performed looking down on the ground. Given the larger altitude of flight compared to terrain elevation variations (except for steep mountains) and limited swath width, point density tends to be more uniform than mobile LIDAR. Mobile LIDAR systems will collect data more densely close to the scanner path and less dense farther from the scanner path.
• The laser footprint on the ground is normally much larger (>0.5m) for airborne LIDAR than for mobile or helicopter LIDAR (few mm to cm). This leads to more horizontal positioning uncertainty with airborne LIDAR.
• ALS generally will have a better (more orthogonal) view (i.e., look angle) of gently sloping or flat terrain (e.g., the pavement surface) compared to that of a mobile LIDAR system (depending on how the mobile laser scanner is oriented). This means that MLS systems will likely miss bottoms of steep ditches that cannot be seen from the roadway.  However, mobile LIDAR systems will have a better view of steep terrain and sides of structures (e.g., Mechanically Stabilized Earth (MSE) walls, cliff slopes). Jersey barrier will block line of sight and create data gaps on the opposing side.  Some projects may benefit from integrated mobile, static, and airborne data collection.
• MLS can capture surfaces underneath bridges and in tunnels.
• MLS is limited in collecting data within a short range (typically 100 m) of navigable roadways. Airborne platforms have more flexibility of where they can collect data.
• For MLS projects, accuracy requirements are the most significant factor relating to project cost. For ALS, acquisition costs generally control the overall project cost.
• For MLS, the GNSS measurements are the major error source; whereas for ALS the IMU and laser foot print size are the major error sources (except for low-flying helicopter LIDAR).

Similarities between the systems include:

• Both acquire data kinematically using similar hardware components (GNSS, IMU, and LIDAR).
• Both capture a point cloud.
• Both systems typically provide laser return intensity (return signal strength) information for each laser return.
• Each point is individually geo-referenced with both systems.
• While MLS can offer significantly improved horizontal accuracy due to look angle, both systems can provide data with high vertical accuracy.
• Both systems can simultaneously acquire imagery and scan data.

Figure 6: Comparison of Airborne and mobile LIDAR systems

9.3      Calculation of ground coordinates from MLS data

Calculation of ground coordinates for objects from laser scanning system observations have been well documented in the literature, (e.g., Baltsavias, 1999; Glennie, 2007). Coordinates on the ground can be calculated by combining the information from the laser scanner, integrated GPS/INS navigation system and calibration parameters (Figure 7). The target coordinate equation is given as:

In examining equation (1), it becomes evident that all terms on the right hand side of the equation contain errors in their determination. Therefore, we can alternatively express the equation as:

The above formula shows that the ground coordinate calculated for the laser return is dependent upon 14 observed parameters. The 14 parameters are:

• X(1), Y(2), Z(3) location of the navigation sensor. These position values are given by the GNSS and IMU navigation subsystem.
• ω (4), φ(5), κ(6) are the roll, pitch and yaw of the sensor with respect to the local level frame. These values are given by the IMU navigation subsystem.
• dω (7), dφ(8), dκ(9), are the boresight angles which align the scanner frame with the IMU body frame. These values must be determined by a system boresight calibration (see, e.g., Morin (2002) or Toth (2002)).
• α(10) and d(11) are the scan angle and range measured and returned by the laser scanner assembly
• l_x(12), l_y(13), l_z(14) are the lever arm offsets from the navigation origin (IMU origin) to the measurement origin of the laser scan assembly. These values must be determined by measurement or system calibration.

Figure 7: Coordinate transformation for MLS.

Next Section >> Chapter 10: Accuracy of Components