3D Surveying of Underground Built Heritage: Opportunities and Challenges of Mobile Technologies
Abstract
:1. Introduction
- agile: cumbersome instruments should be avoided in underground contexts with frequent setbacks and scarce accessibility;
- cost-effective: the best results should be achieved with reasonable costs;
- easy to use: these technologies are also often managed by non-expert staff;
- affordable: CH generally suffers a generalized lack of resources hence, low-cost solutions are preferable.
- experiments performed in a real large scenario using a specific experimental protocol, considering both commercial off-the-shelf and prototype solutions as well as visual and LiDAR methods;
- an accuracy evaluation performed with the aid of reference ground truth data (based on TLS acquisitions), which allowed discussion of the costs/benefits to be argued in a more reliable way, besides providing quantitative results;
- results suitable for defining best practices for exploiting mobile systems in UBH settings, defined considering the different factors making such scenarios challenging.
2. Related Works
2.1. 3D Digitization of Underground Built Heritage
2.2. SLAM and Mobile Technologies
3. Materials and Methods
3.1. Case Study
3.2. Tested Mobile Mapping Systems
- KAARTA Stencil 2–16 [57] is a commercial mobile scanner. It mounts a LiDAR (Velodyne VLP-16), a low-resolution color camera, a low-cost MEMS (Micro Electro Mechanical Systems) IMU, and a computer for real-time processing. VLP-16 has a 360° field of view with a 30° azimuthal opening with a band of 16 scan lines. The data acquisition is based on setting the configuration parameters that vary for the type of environment detected, mainly between outdoor and indoor. These parameters include the voxelSize, namely the resolution of the point cloud in the map file, cornerVoxelSize, surfVoxelSize, sorroundVoxelSize, which indicate the resolution of the point cloud for scan matching and display, and blindRadius, that is the minimum distance of the points to be used for the mapping. This laser device is versatile and can be mounted on any mobile platform. For underground environments, it can be supported by a hand-held pole. The progress of the scanning can be monitored in real-time via an external monitor attached with a USB cable.
- GeoSLAM Zeb Horizon [58] is a commercial and hand-held mobile scanner, mounting the same LiDAR and IMU systems of KAARTA Stencil 2–16. As an accessory, the ZEB Cam is a color camera for GeoSLAM’s ZEB Horizon, embedding a Hawkeye Firefly 8SE action video camera. The image data collected by the camera can be viewed alongside the 3D point cloud created by the ZEB Horizon and used to extract contextual information. The process of collecting data using the Zeb Horizon scanning system is highly automated. The raw laser data is co-registered into a consistent 3D point cloud by the internal SLAM algorithm, assuming a collaborative scene with distinctive geometric features. While KAARTA Stencil 2–16 is able to process data in real-time, the data acquired by GeoSLAM Zeb Horizon are processed using the GeoSLAM Hub processing software. Therefore, an additional accessory of GeoSLAM devices in a data logger to save the acquired data.
- GuPho [59] is a research prototype developed by the 3DOM unit of FBK. Unlike the other two portable MMSs, GuPho is a pure vision device. The 3D reconstruction is therefore obtained from the acquired images with photogrammetry and dense image matching. The system is composed of two synchronized stereo cameras, an embedded pc (Raspberry Pi 4), a smartphone, a battery pack and an illuminator. GuPho integrates a custom Visual SLAM algorithm based on OPEN-V-SLAM [60] to keep track of the acquisition trajectory, filter out redundant images, display the sparse 3D reconstruction of the area in real-time, and provide real-time feedback and warnings on the image acquisition, such as motion-blur and achieved Ground Sample Distance (GSD). The final dense 3D reconstruction is obtained after the survey, in post-processing, leveraging the image orientations estimated in real-time by the Visual SLAM algorithm to speed up the process. Given the narrow and complex structure of the Camerano caves, the system was configured with fisheye lenses (focal length of 1.85 mm and field of view of almost 180°) to maximize the view coverage of the images.
3.3. Data Acquisition and Processing
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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KAARTA Stencil 2–16 | GeoSLAM Zeb Horizon | GuPho | |
---|---|---|---|
| | | |
Acquisition parameters configuration | Scan registration: | Default parameters | Target GSD set to 5 mm Automatic exposure time |
voxelSize: 0.1 m | |||
blindRadius: 1 m | |||
Laser mapping: | |||
cornerVoxelSize: 0.1 m | |||
surfVoxelSize: 0.2 m | |||
surroundVoxelSize: 0.3 m | |||
Acquisition time | ~8 min | ~12 min | Trajectory 1: ~30 min Trajectory 2: ~10 min |
Trajectory leght | ~295 m | ~380 m | Trajectory 1: 290 m Trajectory 2: ~114 m |
Average speed | ~0.612 m/s | ~0.528 m/s | Trajectory 1: ~0.151 m/s Trajectory 2: ~0.185 m/s |
Processing time | Real-time processing (processor embedded) | ~ 25 min (GeoSLAM Hub) | Trajectory 1: |
Bundle adjustement optimization: ~177 min | |||
Dense matching: ~438 min | |||
Trajectory 2: | |||
Bundle adjustement optimization: ~7 min | |||
Dense matching: ~80 min | |||
Hardware | Intel NUC 7i7 Quad Core | i7 8700K, Intel Core ™ 32 GB RAM | i7 6800K, Nvidia 1070 8GB 24 GB RAM |
1. Camerone Cave | C2C | C2M | ||
---|---|---|---|---|
Mean | st. dev. | Mean | st. dev. | |
KAARTA Stencil 2–16 | 0.0715 | 0.1046 | −0.0121 | 0.1289 |
GeoSLAM Zeb Horizon | 0.0085 | 0.0147 | −0.0052 | 0.0149 |
GuPho | 0.0099 | 0.0196 | 0.0013 | 0.0245 |
2. Corridor with Niches | C2C | C2M | ||
---|---|---|---|---|
Mean | st. dev. | Mean | st. dev. | |
KAARTA Stencil 2–16 | 0.0284 | 0.0392 | 0.0132 | 0.0484 |
GeoSLAM Zeb Horizon | 0.0077 | 0.0155 | 0.0038 | 0.0172 |
GuPho | 0.0129 | 0.0254 | −0.0036 | 0.0292 |
3. Corridor with Stairs | C2C | C2M | ||
---|---|---|---|---|
Mean | st. dev. | Mean | st. dev. | |
KAARTA Stencil 2–16 | 0.0185 | 0.0252 | −0.0005 | 0.0324 |
GeoSLAM Zeb Horizon | 0.0093 | 0.0075 | 0.0058 | 0.0109 |
GuPho | 0.0122 | 0.0159 | −0.0037 | 0.0199 |
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Di Stefano, F.; Torresani, A.; Farella, E.M.; Pierdicca, R.; Menna, F.; Remondino, F. 3D Surveying of Underground Built Heritage: Opportunities and Challenges of Mobile Technologies. Sustainability 2021, 13, 13289. https://doi.org/10.3390/su132313289
Di Stefano F, Torresani A, Farella EM, Pierdicca R, Menna F, Remondino F. 3D Surveying of Underground Built Heritage: Opportunities and Challenges of Mobile Technologies. Sustainability. 2021; 13(23):13289. https://doi.org/10.3390/su132313289
Chicago/Turabian StyleDi Stefano, Francesco, Alessandro Torresani, Elisa M. Farella, Roberto Pierdicca, Fabio Menna, and Fabio Remondino. 2021. "3D Surveying of Underground Built Heritage: Opportunities and Challenges of Mobile Technologies" Sustainability 13, no. 23: 13289. https://doi.org/10.3390/su132313289