(This article is based on a peer reviewed paper written by I. Toschi, et al)
Mobile Mapping System (MMS) is nowadays an emerging technology, whose development began in the late 1980s and is constantly growing. From a technological point of view, MMS is a multi-sensor system, that consists mainly of three components: mapping sensors (active and/or passive 3D imaging systems), navigation/positioning sensors (IMU/GNSS) and a control unit, that synchronizes and integrates the acquisition of geometric/positioning information. All sensors are integrated on a rigid moving platform (e.g. vans, cars, trains, boats, snow mobile sledges, people, etc.), whose trajectory is computed and finally used to produce geo-referenced 2D/3D data. Land-based mobile laser scanners mounted on vans or cars represent the best and a cost-effective solution for capturing 3D point clouds of urban areas (Fig. 1).
These X, Y, Z measurements, usually photo-textured with high resolution digital images, can then be viewed, navigated, measured and analyzed into modelling and CAD design software for feature extraction (e.g. road cross section), mapping and visualization purposes. The high speed data capture, huge point density, high survey efficiency and safety represent relevant benefits of MMS technology that is gaining more and more importance in many application fields such as civil engineering and construction, pipeline design, road inventory, environmental applications and cultural heritage.
Because of the continuous developments in both scanning and navigation technologies, the landscape of mobile mapping systems is rapidly varying and evolving. Table 1 provides a review of the main present-day commercial systems available in the market; besides them, several other solutions internally developed at research centers and SMEs are also emerging.
| Supplier | Name | Laser Scanner | IMU/GNSS | Digital Camera | ||
| Sensor(s) | Range | Accuracy | Absolute Pos. (1) | Resolution | ||
| TOPCON | IP-S3 | 1 scanner | 100 m, @ ρ100% | 50 mm, @ 10 m (1σ) | 0.015-0.025 m | Spherical camera,
8000 x 4000 px |
| TRIMBLE | MX8 | 1-2 VQ-250 | 500 m, @ ρ80% | 10 mm, @ 50 m (1σ) | 0.020-0.025 m | Up to 7 cameras, 5 Mpx |
| 1-2 VQ-450 | 800 m, @ ρ80% | 8 mm, @ 50 m (1σ) | ||||
| 3D Laser Mapping | Street Mapper | 1-2 VUX-1HA | 400 m, @ ρ80% | 5 mm, (1σ) | 0.050 m | Panoramic camera, 12 Mpx |
| RIEGL(2) | VMX-250 | 2 VQ-250 | 500 m, @ ρ80% | 10 mm, @ 50 m (1σ) | 0.020-0.050 m | Up to 6 cameras, 5 Mpx |
| Renishaw | Dynascan S250 | 1-2 scanner(s) | 250 m | 10 mm, @ 50 m (1σ) | 0.020-0.050 m | – |
| TELEDYNE
OPTECH | Lynx SG1 | 2 scanners | 250 m, @ ρ10% | 5 mm, (1σ) | 0.050 m | Up to 5 cameras, 5 Mpx
and/or panoramic camera |
| Lynx MG1 | 1 scanner | 0.200 m | ||||
| MITSUBISHI ELECTRIC | MMS-X | 2-4 standard scanners | 65 m | Standard scanner: 10 mm, @ 7 m (1σ)
Long-range scanner: 10 mm, @ 80 m (1σ)
| 0.060 m | Up to 6 cameras, 5 Mpx |
| MMS-X320R | 2 standard scanners / 1 long-range scanner | 65 m / 200 m | 3 cameras, 5 Mpx | |||
| MMS-K320 | 2 standard scanners | 65 m | 3 cameras, 5 Mpx | |||
| Leica Geosystems | Leica Pegasus:
Two | ZF 9012 | 119 m | 0.9 mm, @ 50 m, ρ80% (1σ) | 0.015-0.020 m | 8 cameras, 2000 x 2000 px |
| Leica Scanstation P20 | 120 m, @ ρ18% | 6 mm, @ 100 m (1σ) | ||||
Tab. 1 – Most common commercial Mobile Mapping Systems and related components ((1) post-processed accuracy values given as RMS; (2) technical characteristics of RIEGL VMX-450 are provided below).
MOBILE MAPPING SURVEY WITH RIEGL VMX-450
The MMS RIEGL platform (Fig. 2 and Tab. 2) integrates two synchronously operated VQ-450 laser scanners, a portable control unit (VMX-450-CU) and IMU/GNSS navigation hardware. The system (2 scanners) is able to measure up to 1.1 million points and 400 profiles per second, providing extremely dense and feature-rich data even at high driving speed. Furthermore, it exploits the RIEGL echo signal digitization technology and online waveform processing, resulting in a high penetration capability of obstructions (e.g. fences and vegetation). The platform is also equipped with the modular VMX-450-CS6 camera system that complements the acquisition of geometric data with the recording of time-stamped images.
| Sensor | VQ-450 |
| Measuring principle | Time of Flight |
| Laser wavelength | Near infrared |
| Laser measurement rate | 300 – 1100 kHz |
| Maximum range | 140 – 800 m |
| Minimum range | 1.5 m |
| Accuracy | 8 mm, 1σ |
| Precision | 5 mm, 1σ |
| Sensor | IMU/GNSS |
| Absolute position | 0.020 – 0.050 m |
| Roll and pitch | 0.005 º |
| True heading | 0.015 º |
| Sensor | VMX-450-CS6 |
| Resolution | 5 Mpx |
| Sensor size | 2452 x 2056 px |
| Pixel size | 3.45 μm |
| Nominal focal length | 5 mm |
Tab. 2 Technical characteristics of the RIEGL VMX-450 system
A van-based MMS survey was planned and carried out in the city center of Trento (Italy) on September 22nd, 2014 in order to (i) assess the RIEGL VMX-450 system performance in a challenging scenario that includes typical urban canyons and complex building typologies and (ii) evaluated the use of MMS data for road inventory, 3D city modelling and cartographic mapping. An area of about 700 m west to east by 500 m north to south was covered, including the Duomo square and several narrow streets (average width of 3-4 m) around it. The collected point clouds feature a mean spatial resolution of 2-3 cm and were delivered as LAS file format in WGS84 global coordinate system. No control point information was included within the post-processing adjustment.
PRECISION AND ACCURACY EVALUATION
The performance test is carried out using ‘reference’ data derived from a photogrammetric and a Terrestrial Laser Scanner (TLS) survey. When comparing point clouds acquired by different instruments, some issues should be considered – i.e. (i) a proper error budget computation should be defined since each system has its own sources of uncertainties and sensitivity to outlier presence; (ii) the compared 3D points are not exactly corresponding to each other; (iii) the object surfaces are not equally digitized as the acquisition positions are different. Due to these reasons, a methodology is carefully designed in order to analyze the precision and the accuracy achieved by the MMS system with advanced statistical methods. The workflow adopted in this study relies on a Gaussian pre-assessment, followed by an outlier filtering process. Finally, non-parametric statistical models are applied in order to achieve a robust estimation of the error dispersion.
The comparisons between point clouds acquired by the two laser scanners on the MMS at the same epoch deliver a Gaussian estimation of the MAD in the order of ± 5.2 mm, which is consistent with the precision (1σ value) quoted by the datasheet. The error increases up to ± 8.6 mm if the acquisitions in two different epochs are compared. These differences can be possibly traced back to geo-referencing errors and surface-related problems.
The accuracy assessment of one single point cloud highlights the good metric potentialities of the RIEGL mobile system. Errors estimated for the buildings in the Duomo square are characterized by an average dispersion of ± 5.9 mm (MMS vs. Photogrammetry, Fig. 3) and ± 3.6 mm (MMS vs. TLS). These values are consistent with the uncertainty quoted by the MMS manufacturer (8 mm, 1σ). As expected, errors increase if MMS mapping is performed in narrow streets with high buildings and more challenging obstacles. However, the computed value of ± 7.4 mm in such areas still represents an acceptable trade-off.
CONCLUSIONS
Nowadays the several commercial MMS solutions available on the market show the best example of sensor integration and cost-effective acquisition of geo-referenced spatial data with a combination of digital imaging devices, long-range laser scanners and GNSS/IMU positioning sensors. The growing interest in the field of mobile mapping, also shown by international organizations as EuroSDR, should be supported by efforts aimed at addressing the issue of 2D/3D data management. Furthermore, the accuracy requirements for the acquired data is substantially different in each application with road or rail infrastructure surveying being much more demanding in this particular respect. Thus, every scanner specification should be taken into account in order to obtain the best solution according to the intended application. Finally, a proper evaluation procedure should be carried out by adopting external reference data sets and a rigorous statistical approach.
