3D Depth Imaging for Dynamic Container Auto-Configuration
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Solution Overview
Problem
The transportation industry faces challenges in managing the loading of shipping containers due to various sizes and configurations, leading to inefficiencies and inaccuracies in tracking loading metrics, particularly with manual position tracking that is time-consuming and prone to errors caused by container and camera shifts during the loading process.
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 image data, automatically determine container point clouds, and generate accurate digital bounding boxes to remove interference and provide real-time localization, even during shifts in container positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual position tracking is used, then implementation is simple, but accuracy deteriorates due to time consumption and errors from container/camera shifts
Solution Approach 1:
The patent replaces manual mechanical tracking with an automated 3D depth imaging system that captures container positions and generates point clouds. The system automatically localizes containers by matching point cloud features with container models, eliminating manual intervention and its associated errors while providing real-time position updates during loading operations.
Solution Approach 2:
The system enables self-service localization by automatically capturing 3D images, generating point clouds, and determining container positions without human assistance. The automated processing pipeline includes point cloud generation, feature extraction, model matching, and position calculation, all performed by the system itself to eliminate manual tracking errors.
2Speed
If direct 3D matching technique is employed, then real-time localization is achieved, but reliability deteriorates due to sensitivity to partial structures and high computation complexity
Solution Approach 1:
The patent segments the container structure into distinct geometric features (edges, corners, surfaces) and processes them separately through a multi-stage matching pipeline. The system divides the point cloud into relevant container portions, extracts specific geometric features, and matches them independently before integrating results, which improves both speed and reliability by avoiding processing of irrelevant partial structures.
Solution Approach 2:
The system applies different processing qualities to different parts of the point cloud based on their relevance. High-precision feature extraction and matching are applied to critical container structures (edges, corners), while less critical areas receive simplified processing. This selective approach maintains matching stability while reducing computation complexity for real-time performance.
3Device complexity
If point cloud clustering is used, then processing is simplified, but measurement precision deteriorates due to sensitivity to noise and small object interference
Solution Approach 1:
The patent performs preliminary noise filtering and interference removal before clustering operations. The system pre-processes the point cloud by identifying and eliminating points corresponding to loaders, packages, and other moving objects before attempting clustering. This preliminary action ensures that subsequent clustering operates only on stable container structure points, maintaining precision while keeping processing relatively simple.
4Speed
If 2.5D template matching is employed, then real-time localization is achieved, but reliability deteriorates due to package and loader interference creating incorrect clustering results
Solution Approach 1:
The patent extracts and removes interfering elements (loaders, packages, moving objects) from the point cloud before performing template matching. The system identifies these elements through motion detection and spatial analysis, separates them from the container structure, and conducts matching operations only on the cleaned container point cloud. This extraction of harmful factors ensures reliable localization even in dynamic loading environments.
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
This solution enables fast and efficient real-time localization and diagnostics, improving the accuracy and efficiency of loading operations by eliminating errors associated with manual tracking and container shifts, and simplifying calibration processes for multiple container types.
Implementation Method 1
a 3D-depth camera configured to capture 3D image data of a container
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.