LiDAR Camera Stockpile Volume Estimation Without GPS
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Solution Overview
Problem
Existing methods for estimating the volume of large stockpiles of materials like salt are inefficient, requiring excessive human and computational time, are costly, and perform poorly in low light or GPS-denied environments, with inaccuracies and accessibility issues.
Innovation Solution
A portable system using LiDAR sensors and a camera for 3D volume estimation, employing sparse data analysis and automated image-aided registration techniques to determine the rotation and translation of the system, allowing for accurate volume calculation without continuous GPS tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods (tape measures, counting truck loads, photographic imaging, static laser scanning) are used to estimate stockpile volume, then the measurement can be performed with simple equipment, but the process requires large amounts of human and computational time and produces poor accuracy
Solution Approach 1:
The patent replaces manual mechanical measurement methods (tape measures, manual counting) with automated LiDAR scanning and image processing systems. The LiDAR sensors automatically capture 3D point cloud data of the stockpile, and computer vision algorithms automatically process this data to calculate volume, eliminating the need for manual measurement and reducing both time and labor requirements while improving accuracy.
Solution Approach 2:
The patent changes the measurement parameters from traditional 2D photographic images to 3D point cloud data captured by LiDAR. This parameter change enables direct volumetric analysis of the stockpile geometry, allowing for more accurate volume estimation compared to traditional photographic imaging methods while reducing the time needed for analysis.
2Measurement precision
If expensive systems with continuous GPS tracking and encoders are used to track sensor location and orientation, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts and removes the complex GPS tracking and encoder systems from the measurement apparatus. Instead of continuously tracking sensor location and orientation using expensive GPS encoders, the system uses LiDAR point cloud data and image processing to determine the necessary spatial information, thereby reducing device complexity and cost while maintaining measurement precision.
Solution Approach 2:
The patent creates a digital 3D copy of the stockpile environment through LiDAR point cloud data and image processing. This digital model allows the system to infer location and orientation information without requiring continuous GPS tracking, effectively copying the necessary spatial information from the physical environment into a computable format that simplifies the hardware requirements.
3Device complexity
If LiDAR sensors are used to collect sparse data from multiple scans, then the system becomes more portable and less expensive, but the data sparsity makes registration and volume calculation more difficult
Solution Approach 1:
The patent segments the LiDAR data collection process into multiple discrete scans from different positions and orientations. Each scan captures a portion of the stockpile environment, and the segmentation allows the system to process and register these individual scans separately before combining them into a complete 3D model, making the registration of sparse data more manageable and accurate.
Solution Approach 2:
The patent introduces image processing and computer vision algorithms as intermediaries between the sparse LiDAR point cloud data and the final volume calculation. These intermediary algorithms process the sparse data from multiple scans, perform registration by matching features across scans, and generate the complete 3D model, thereby overcoming the difficulties posed by data sparsity while maintaining system portability and affordability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides accurate volume estimation quickly and efficiently, reducing costs and overcoming environmental limitations like low light and featureless surfaces, achieving results comparable to more expensive systems.
Implementation Method 1
A first LiDAR sensor (e.g., a Velodyne VLP-16 LiDAR sensor) is used to produce a first set of LiDAR scan data corresponding to a first environment
Implementation Method 2
A camera (e.g., an RGB camera, such as a GoPro Hero 9 RGB camera) is used to capture a set of images corresponding to the environment
Data Source
AI summary
Systems and methods for determining the volume of a stockpile are disclosed. Embodiments include one or more detectors (sensors and/or cameras) and processing of the data gathered from the detectors in a manner that provides accurate volume estimates without requiring the exact location of the detectors. Some embodiments utilize one or more image cameras and LiDAR sensors to obtain data about the stockpile and compute the volume of the stockpile using one or more of the following procedures: segmentation of planar features from individual scans; image-based coarse registration of sensor scans at a single station; feature matching and fine registration of sensor point clouds from a single station; coarse registration of point clouds from different stations; feature matching and fine registration of sensor point clouds from different stations; and digital surface model generation for volume estimation. Some embodiments are connectable to extendable mounts and are very easy to operate.