Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring
Abstract
:1. Introduction
- (1)
- An extensive literature review that describes different methods and applications for the monitoring of terrestrial transportation networks using data collected from Mobile Mapping Systems equipped with LiDAR sensors, with a focus on infrastructure assets whose analysis is relevant in the context of transport network resilience.
- (2)
- A descriptive summary of different laser scanner systems and their components, together with a comparison of commercial systems.
- (3)
- A special focus on railway network monitoring, which in this work is classified based on the application and extensively reviewed.
- (4)
- A remark on the most recent trends regarding methods and algorithms, with a focus on supervised learning and its most recent trend, deep learning.
- (5)
- A discussion on the main challenges and future trends for laser scanner technologies.
2. Laser Scanner Technology
2.1. Laser Scanner System Components
2.1.1. LiDAR
2.1.2. Positioning and Navigation Systems
2.2. Performance Of Laser Scanning Systems
Comparison of Monitoring Technologies
2.3. Types of Laser Scanner Systems
2.4. Comparison of Commercial Laser Scanners
3. State-of-the-Art regarding LiDAR-Based Monitoring of Transport Infrastructures
3.1. Road Network Monitoring
3.1.1. Road Surface Monitoring
- Road surface extraction based on its structure: A common approach for road surface extraction relies on the definition of road edges that delineate its limits. This approach has been evolving since the beginning of this decade. Ibrahim and Lichti [50] propose a sequential analysis that segments the ground based on the point density and then using a Gaussian filtering to detect curbs and extract the road surface afterwards (Figure 3a). These steps are analogous in similar works, changing the curb detection method. Some works perform a rasterization (projection of the 3D point cloud in a gridded XY plane generating two dimensional geo-referenced feature (2D GRF) images) and detect curbs using image processing methods such as the parametric active contour or snake model [51,52] or image morphology [53,54]. Guan et al. [55] generate pseudo-scan lines in the plane perpendicular to the trajectory of the vehicle to detect curbs by measuring slope differences. Differently, a number of approaches have been developed for curb detection directly in 3D data using point cloud geometric properties such as density and elevation [56], or derived properties such as saliency, which measures the orientation of a point normal vector with respect to the ground plane normal vector [57] and has been successfully used to extract curbs or salient points in different works [58,59]. Xu et al. [60] use an energy function based on the elevation gradient of previously generated voxels (3D equivalent of pixels) to extract curbs, and a least cost path model to refine them. Using voxels allows one to define local information by defining parameters within each voxel and to reduce the computational load, so they are commonly used for road extraction [61,62]. Hata et al. [63] propose a robust regression method named Least Trimmed Squares (LTS) to deal with occlusions that may cause discontinuities on road edge detection. A different approach can be found in Cabo et al. [64], where the point cloud is transformed into a structured line cloud and lines are grouped to detect the edges (Figure 3b). Although good results can be found among these works, most of them rely on curbs to define road edges, hence the extraction of the road surface will not be robust when it is not delimited by curbs, as is the case in most non-urban roads.
- Road surface extraction based on feature calculation: A different approach for road surface extraction is based on previous knowledge about its geometry and contextual features, which can be identified on the 3D point cloud data. Guo et al. [65] filter points based on their height with respect to the ground and then extract the road surface via TIN (Triangulated Irregular Network) filter refinement. Generally, the elevation coordinate of the point cloud is the key feature that is employed for road surface extraction: Serna and Marcotegui [66,67] defined the -flat zones algorithm, which analyses the local height difference of the point cloud projected on the XY plane. Additionally, Fan et al. [68] employ a height histogram for detecting ground points as a pre-processing step on an object detection application. Another feature that is commonly used is the roughness of the road surface. Díaz–Vilariño et al. [69] present an analysis of roughness descriptors that are able to classify different types of road pavements (stone, asphalt) with accuracy. Similarly, Yadav et al. [70] employ roughness, together with radiometric features (assuming uniform intensity as a property of the road) and 2D point density, to delineate road surfaces from non-road surfaces. As it was the case for curb detection methods, there are scan line-based methods that rely on the point topology [71] or density [72] across the scan line for extracting the road surface.
3.1.2. Off-Road Surface Monitoring
3.1.3. Current and Future Trends
3.2. Railway Network Monitoring
3.2.1. Railway Inventory and 3D Modelling
3.2.2. Rails
3.2.3. Power Line
3.2.4. Signalization
3.2.5. Inspection
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Manufacturer | RIEGL | Teledyne Optech | FARO | Velodyne | SICK | Hexagon-Leica |
---|---|---|---|---|---|---|
LiDAR Model | VUX-1HA | Lynx HS300 | Focus 350 | Alpha Puck | LMS511 | ScanStation P50 |
Measurement principle | ToF | ToF | Phase difference | ToF | ToF | Phase difference |
Minimum range | 1.2 m | 0.6 m | 0.4 m | |||
Maximum range | 420 m @ 300 kHz | 250 m | 350 m | 300 m | 80 m | >1 km |
Range accuracy | 5 mm | 0.30 mm @ 25 m | Up to 3 cm | 1.2 mm | ||
Range precision | 3 mm | 5 mm | ||||
PRF (pulse repetition frequency) | 300–1000 kHz | 75–800 kHz | 122–976 kHz | 2400 kHz | Up to 1000 kHz | |
Scan frequency | 10–250 Hz | 300 Hz | 97 Hz (V) | 25–100 Hz | ||
Laser wavelength | Near infrared | 1550 nm | 903 nm | 905 nm | 1550 nm | |
Field of View | 360° | 360° | 300° (V) 360° (H) | 40° (V) 360° (H) | 190° | 290° (V) 360° (H) |
Angular resolution | 0.001° | 0.01° | 0.11° (V) 0.1–0.4° (H) | 0.167° | 0.002° (V) 0.002° (H) | |
Data Sources | [40] | [41] | [42] | [43] | [44] | [45] |
Processing Strategy | |||
---|---|---|---|
Based on road structure (road edge delineation) | Based on feature calculation | ||
Data structure | 3D point cloud | [50,56,57,58,59,60,61,62,63] | [65,68,69,70,72] |
2D GRF | [51,52,53,54] | [66,67] | |
Scan lines | [64] | [71] |
Processing Strategy | ||||
---|---|---|---|---|
Detection Process | Classification Process | |||
Data structure | 2D GRF | Morphology | Adaptive thresholding | - Template matching [65,73] - Neural Networks [73] - Deep Learning [81] |
[65,74,75,80] | [73,76,88] | |||
3D point cloud | - Spatial density filter: [79] - Scan line separation: [78] | - Deep Boltzmann Machines [79] | ||
Photogrammetry | - Deep Learning (CNNs) [85,87] |
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Soilán, M.; Sánchez-Rodríguez, A.; del Río-Barral, P.; Perez-Collazo, C.; Arias, P.; Riveiro, B. Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring. Infrastructures 2019, 4, 58. https://doi.org/10.3390/infrastructures4040058
Soilán M, Sánchez-Rodríguez A, del Río-Barral P, Perez-Collazo C, Arias P, Riveiro B. Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring. Infrastructures. 2019; 4(4):58. https://doi.org/10.3390/infrastructures4040058
Chicago/Turabian StyleSoilán, Mario, Ana Sánchez-Rodríguez, Pablo del Río-Barral, Carlos Perez-Collazo, Pedro Arias, and Belén Riveiro. 2019. "Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring" Infrastructures 4, no. 4: 58. https://doi.org/10.3390/infrastructures4040058