3D Depth Imaging for Shipping Container Auto-Configuration
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Solution Overview
Problem
The transportation industry faces challenges in managing the diverse loading strategies and configurations of shipping containers, leading to inefficiencies and inaccuracies in tracking loading metrics due to manual position tracking and interference from loaders and packages, which conventional techniques like 3D matching, point cloud clustering, and 2.5D template matching fail to address effectively.
Innovation Solution
A 3D depth imaging system and method for dynamic container auto-configuration, utilizing a 3D-depth camera and container auto-configuration application to capture and process 3D image data, automatically determine container point clouds, and generate accurate digital bounding boxes to remove interference and localize container positions in real-time, even during shifting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct 3D matching technique is employed to match target point cloud to 3D template point cloud, then localization can be performed, but the technique lacks stable and repeatable results and involves high computation complexity
Solution Approach 1:
The patent segments the container structure into distinct geometric components (front board, side walls, top edges) and processes them separately through hierarchical steps. This segmentation reduces computational complexity by breaking down the complex 3D matching problem into manageable sub-problems while maintaining localization accuracy through systematic refinement of each segment's position.
Solution Approach 2:
The patent performs preliminary actions by first identifying the front board area before determining side walls and top edges. This preliminary identification establishes a reference frame that simplifies subsequent localization steps, reducing overall computation complexity while ensuring stable and repeatable results through a structured sequence of operations.
2Productivity
If point cloud clustering technique is used for localization, then processing can be performed, but it lacks stable and repeatable results and is sensitive to noise and small object interference
Solution Approach 1:
The patent extracts and removes interference elements (loaders, packages, noise) from the point cloud data before performing localization. By taking out these disturbing elements that cause sensitivity in clustering techniques, the system achieves stable and repeatable results while maintaining processing efficiency through targeted extraction rather than comprehensive re-processing.
Solution Approach 2:
The patent implements a dynamic, multi-step localization process that adapts to changing conditions during container loading. The system dynamically identifies front board areas, then uses them as references for subsequent side wall and top edge detection, providing reliable results even as containers and cameras shift position during the loading process.
3Measurement precision
If 2.5D template matching is employed for real-time localization, then localization can be achieved, but it requires intensive computation and suffers from incorrect ground fitting
Solution Approach 1:
The patent transitions from 2.5D template matching to a hierarchical 3D geometric construction approach that builds the container bounding box through sequential identification of front board, side walls, and top edges. This dimensional transformation reduces computation intensity by avoiding intensive 2.5D matching while achieving accurate 3D localization through systematic geometric construction.
Solution Approach 2:
The patent performs preliminary identification of the front board area to establish a reference plane and orientation before determining side walls and top edges. This preliminary action eliminates the need for intensive ground fitting computations by providing a stable reference frame that guides subsequent 3D construction, reducing computation intensity while maintaining localization accuracy.
4Measurement precision
If manual visual checking is used to setup ULD position, then positioning can be performed, but it is time consuming and less efficient
Solution Approach 1:
The system performs automatic self-service localization by capturing images, identifying container features, and computing position information without human intervention. The container itself serves as the reference object, with its geometric features (front board, side walls, top edges) automatically detected and used to establish position, eliminating time-consuming manual visual checking while maintaining high position accuracy.
Solution Approach 2:
The patent replaces manual visual checking with an automated image processing and computer vision system. The mechanical/manual process of visual inspection is substituted with automated capture of images, algorithmic identification of geometric features, and computational determination of position, dramatically reducing setup time while preserving or improving position accuracy.
Data Source
AI summary
Three-dimensional (3D) depth imaging systems and methods are disclosed for dynamic container auto-configuration. A 3D-depth camera captures 3D image data of a shipping container located in a predefined search space during a shipping container loading session. An auto-configuration application determines a representative container point cloud and (a) loads an initial pre-configuration file that defines a digital bounding box having dimensions representative of the predefined search space and an initial front board area; (b) applies the digital bounding box to the container point cloud to remove front board interference data from the container point cloud based on the initial front board area; (c) generates a refined front board area based on the shipping container type; (d) generates an adjusted digital bounding box based on the refined front board area; and (e) generates an auto-configuration result comprising the adjusted digital bounding box containing at least a portion of the container point cloud.