3D Depth Imaging for Shipping Container Fullness
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Solution Overview
Problem
Existing shipping container loading strategies face challenges in efficiently tracking and managing the loading progress and fullness of containers, especially in large facilities, due to manual monitoring methods being inefficient and existing systems requiring extensive algorithm adjustments and separate coding for each container type, which are slow and inaccurate.
Innovation Solution
A 3D depth imaging system that uses templates to automatically determine shipping container fullness by capturing 3D image data and aligning it with pre-defined 3D template point clouds to calculate a fullness score, allowing for quick adaptation to new container types and accurate accounting of various container shapes and sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual monitoring techniques are used to track loading status of shipping containers, then flexibility and adaptability to different container types are maintained, but efficiency and productivity are severely reduced due to the inability to provide updated status in real-time
Solution Approach 1:
The patent replaces manual monitoring mechanisms with an automated optical imaging system that uses 3D depth cameras to capture and process container loading status. The system automatically captures images, processes them through algorithms, and generates real-time fullness metrics, eliminating the time-consuming manual monitoring process while maintaining adaptability to different container types through configurable parameters.
2Measurement precision
If existing systems apply scaling factors to estimate container fullness, then a simple estimation method is provided, but accuracy and reliability are compromised due to the need for extensive experimental calculations and algorithm adjustments for each container type
Solution Approach 1:
The patent changes the fundamental parameters used for fullness estimation by transitioning from 2D scaling factors to 3D depth-based measurements. The system captures three-dimensional images and uses point cloud processing to calculate actual container volume occupancy, providing accurate measurements across different container types without requiring extensive experimental calibration for each specific configuration.
Solution Approach 2:
The patent creates a universal measurement system that can handle multiple container types and configurations through a single standardized 3D imaging approach. The system uses configurable parameters and template matching that can be adapted to different container geometries without requiring complete reconfiguration, making the measurement process both accurate and broadly applicable.
3Adaptability or versatility
If separate software coding is implemented for each shipping container type, then accuracy for specific container types is improved, but adaptability and ease of operation are reduced due to the need for weeks of algorithm adjustment and coding
Solution Approach 1:
The patent performs preliminary actions by pre-configuring the system with configurable parameters and template libraries that can be quickly adapted to different container types. Rather than requiring weeks of algorithm development for each container type, the system uses pre-established 3D processing algorithms with adjustable parameters that can be rapidly configured, significantly reducing the adaptation time while maintaining accuracy.
4Measurement precision
If conventional 2D imaging is used to monitor container loading, then device complexity is kept low, but measurement precision and reliability are insufficient for accurately determining container fullness
Solution Approach 1:
The patent transitions from two-dimensional imaging to three-dimensional depth imaging to accurately measure container fullness. By capturing 3D point clouds and using depth information, the system can calculate actual volume occupancy and provide precise fullness measurements that account for the three-dimensional nature of container loading, significantly improving measurement accuracy despite increased system complexity.
Data Source
AI summary
Three-dimensional (3D) depth imaging systems and methods are disclosed for automatically determining shipping container fullness based on imaging templates. A 3D-depth camera captures 3D image data of a shipping container located in a predefined search space during a shipping container loading session. A container fullness application (app) receives the 3D image data, and determines therefrom a 3D container point cloud representative of a shipping container. An imaging template that defines a 3D template point cloud corresponding to a shipping container type of the shipping container is loaded into memory. A fullness value of the shipping container is determined based on a 3D mapping that is generated from alignment of a 3D container front portion of the 3D container point cloud with a 3D template front portion of the 3D template point cloud.