Image-Aided LiDAR Mapping Platform and Data Processing Strategy for Stockpile Volume Estimation
2. Related Work
- Unlike relying on a system-driven approach using sophisticated, expensive encoders and/or inertial measurement units, the SMART system focuses on a data-driven strategy for stockpile volume estimation using only acquired data from a simple, cost-effective acquisition procedure;
- It is easy to deploy and has the potential of permanent installation in indoor facilities (after suitable modifications of the setup) for continuous monitoring of stockpiles; and,
- Low-cost, high-precision image/LiDAR hybrid technology such as SMART can influence system manufacturers to develop inexpensive stockpile monitoring solutions, which could be even less expensive.
3. SMART System Integration and Field Surveys
3.1. SMART System Components
- LiDAR units: In order to derive a 3D point cloud of the stockpile in question, LiDAR data is first acquired through the VLP-16 sensors. The Velodyne VLP-16 3D LiDAR  has a vertical field of view (FOV) of 30° and a 360° horizontal FOV. This FOV is facilitated by the unit construction, which consists of 16 radially oriented laser rangefinders that are aligned vertically from −15° to +15° and designed for 360° internal rotation. The sensor weight is 0.83 kg and the point capture rate in a single return mode is 300,000 points per second. The range accuracy is ±3 cm with a maximum measurement range of 100 m. The vertical angular resolution is 2° and horizontal angular resolution is 0.1–0.4°. The angular resolution of the LiDAR unit enables an average point spacing within one scan line of 3 cm, and between neighboring scan lines of 30 cm at 5 m range (average distance to the salt surface). Given the sensor specifications, two LiDAR units with cross orientation are adopted to increase the area covered by the SMART system in each instance of data collection. The horizontal coverage of the SMART LiDAR units is schematically illustrated in Figure 2. As shown in this figure, two orthogonally installed LiDAR sensors simultaneously scan the environment in four directions. The 360° horizontal FOV of the VLP-16 sensors implies that the entire salt facility within the system’s vertical coverage is captured by the LiDAR units. In addition to the possibility of covering a larger area of the stockpile, this design allows for scanning surrounding structures, thereby increasing the likelihood of acquiring diverse features in all directions from a given scan. These features (linear, planar, or cylindrical) can be used for the alignment of LiDAR data collected from multiple scans to derive point clouds in a single reference frame.
- RGB camera: The SMART system uses a GoPro Hero 9 camera, which weighs 158 g. The camera has a 5184 × 3888 CMOS array with a 1.4 pixel size and a lens with a nominal focal length of 3 mm. Horizontal FOV of 118° and 69° vertical FOV enable the camera to cover roughly 460 square meters with a 10 m range. A schematic diagram of the camera coverage from the SMART system is depicted in Figure 2. In addition to providing RGB information from the stockpile, images captured by the RGB camera are used to assist the initial alignment process of the LiDAR point clouds collected at a given station. This process will be discussed in detail in Section 4.3.
- Computer module: A Raspberry Pi 3b computer is installed on the system body and is used for LiDAR data acquisition and storage. Both LiDAR sensors are triggered simultaneously through a physical button that has wired connection to the computer module. Once the button is pushed, the Raspberry Pi initiates a 10 s data capture from the two LiDAR units. In the meantime, the RGB camera is controlled wirelessly (using a Wi-Fi connection) through a mobile device, which enables access to the camera’s live view for the operator. All the images captured are transferred to the processing computer through a wireless network. The LiDAR data is transferred from the Raspberry Pi using a USB drive. Figure 3 shows the block diagram of the system indicating triggering signals and communication wires/ports between the onboard sensors and Raspberry Pi module.
- GNSS receiver and antenna: As one of the potential ways to enhance SMART system capabilities, a GNSS receiver and antenna are added as one of the system components. The purpose of the GNSS unit is to provide location information when operating in outdoor environments. The location information serves as an additional input to aid the point cloud alignment from multiple positions of the system. In this study however, data collection is targeted in a more challenging indoor environment. Therefore, GNSS positioning capabilities of the system are not utilized.
- System body: LiDAR sensors, an RGB camera, and a GNSS unit of the SMART system are placed on a metal plate attached to an extendable tripod pole that are together considered as the system body. The computer module and power source are located on the tripod pole. The extendable tripod, with a maximum height of 6 m, helps the system minimize occlusions when collecting data from large salt storage facilities and/or stockpiles with complex shapes.
3.2. System Operation and Data Collection Strategy
3.3. Dataset Description
4. Data Processing Workflow
4.1. System Calibration
4.2. Scan Line-Based Segmentation
- Scans are acquired by spinning multi-beam LiDAR unit(s), i.e., VLP-16;
- LiDAR scans are acquired inside facilities bounded by planar surfaces that are sufficiently distributed in different orientations/locations, e.g., floor, walls, and ceiling;
- A point cloud exhibits significant variability in point density, as shown in Figure 8.
4.3. Image-Based Coarse Registration
4.4. Feature Matching and Fine Registration of Point Clouds from a Single Station
4.5. Coarse Registration of Point Clouds from Multiple Stations
4.6. Volume Estimation
5. Experimental Results and Discussion
5.1. System Calibration Results
5.2. Results of Image-Based Coarse Registration at a Single Station
- Number of matches/projection residuals: For the automated approaches, the number of matches signifies the ability to establish enough conjugate features between two successive images. In the case of manual measurements, few reliable conjugate points with a relatively good distribution are established. The projection residual, which is the RMSE value of differences between the coordinates of projected features from the left to right image and their corresponding features in the right image, can be used to infer the quality of established matches and/or estimated rotation angles. Large projection residual is an indication of high percentage of matching outliers, and consequently, inaccurate estimates of the incremental pole rotation angles.
- Incremental pole rotation angles (, , and ): Considering the results form manual measurements as a reference, this criterion shows how accurately the automated approaches can estimate the incremental pole rotation between two scans. As mentioned earlier, the nominal incremental pole rotation between two scans (i.e., , , and ) are (0.0°, 0.0°, and −30.0°), respectively.
- Processing time: For the automated approaches, this refers to the processing time for feature detection, descriptor generation, and matching steps. In case of manual measurements, this refers to the approximate time required for manually identifying point correspondences between the two images.
5.3. Fine Registration Results
5.4. Stockpile Volume Estimation
7. Conclusions and Recommendations for Future Work
- The integrated hardware system composed of an RGB Camera, two LiDAR units, and an extendable tripod. This system addresses the limitations of current stockpile volume estimation techniques by providing a time-efficient, cost-effective, and scalable solution for routine monitoring of stockpiles with varying sizes and shape complexity.
- An image-aided coarse registration technique has been designed to mitigate challenges in identifying common features in sparse LiDAR scans with insufficient overlap. This new approach uses the designed system characteristics and operation to derive a reliable set of conjugate points in successive images for precise estimation of the incremental pole rotation at a given station.
- A scan line-based segmentation (SLS) approach for extracting planar features from spinning multi-beam LiDAR scans has been proposed. The SLS can handle significant variability in point density and provides a set of planar features that could be used for reliable fine registration.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Scalability||Featureless Surfaces||Cost-Effective||Indoor||Operator’s Skill|
|SMART||Faro Focus (TLS)||Size (W × L × H)|
|Number of Stations||Number of Scans Per Station||Number of|
|Lebanon unit||2||7||2||26 m × 48 m × 10.5 m|
|US-231 unit||1||7||3||30.5 m × 25.5 m × 10 m|
|Sensor||Lever-Arm Offset||Boresight Angles|
|ΔX (m)||ΔY (m)||ΔZ (m)||Δω(°)||Δφ(°)||Δκ(°)|
|LiDAR Unit 1||0||−0.20||0||42||0||0|
|LiDAR Unit 2||−0.165||−0.029||−0.072||−7.102||−57.144||−104.146|
|Image-Based Coarse Registration Technique||Stereo|
|Number of Matches||Projection|
|Pole Δω(°)||Pole Δφ(°)||Pole Δκ(°)||Processing Time (Second)|
|Dataset||Total Number of Planar Features Used||Total Number of Points||Point Density Range (Points/m2)||RMSE of Normal Distance (m)||LiDAR Ranging Noise (m)|
|Dataset||Occlusion (%)||SMART Volume||TLS Volume||Error (%)|
|Volume from SMART||Reference/TLS||Error (%)|
|Left big pile||798||808.9||10.9||1.4|
|Left small pile||17.2||17.5||0.3||1.7|
|Volume from SMART||Reference/TLS||Error (%)|
|Right big pile||603.9||604.1||0.2||~0.0|
|Right small pile||17.04||17.88||0.84||4.7|
|Platform||Approximate Cost (USD)|
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Manish, R.; Hasheminasab, S.M.; Liu, J.; Koshan, Y.; Mahlberg, J.A.; Lin, Y.-C.; Ravi, R.; Zhou, T.; McGuffey, J.; Wells, T.; et al. Image-Aided LiDAR Mapping Platform and Data Processing Strategy for Stockpile Volume Estimation. Remote Sens. 2022, 14, 231. https://doi.org/10.3390/rs14010231
Manish R, Hasheminasab SM, Liu J, Koshan Y, Mahlberg JA, Lin Y-C, Ravi R, Zhou T, McGuffey J, Wells T, et al. Image-Aided LiDAR Mapping Platform and Data Processing Strategy for Stockpile Volume Estimation. Remote Sensing. 2022; 14(1):231. https://doi.org/10.3390/rs14010231Chicago/Turabian Style
Manish, Raja, Seyyed Meghdad Hasheminasab, Jidong Liu, Yerassyl Koshan, Justin Anthony Mahlberg, Yi-Chun Lin, Radhika Ravi, Tian Zhou, Jeremy McGuffey, Timothy Wells, and et al. 2022. "Image-Aided LiDAR Mapping Platform and Data Processing Strategy for Stockpile Volume Estimation" Remote Sensing 14, no. 1: 231. https://doi.org/10.3390/rs14010231