3D Depth Camera Orientation Guide for Shipping Containers
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Solution Overview
Problem
Traditional analytics systems for shipping container loading, such as those using load monitoring units (LMUs), face inefficiencies due to manual orientation processes that are time-consuming and inaccurate, and existing techniques like direct 3D matching and point cloud clustering are not robust, sensitive to interference, and produce erroneous results.
Innovation Solution
The implementation of a 3D depth imaging system that automatically assesses and configures camera orientation using a 3D point cloud and 2D depth image template matching algorithm, providing real-time accurate orientation for LMU installation by segmenting the container's planes and calculating occlusion ratios to guide the installation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of previously captured images is used to orient LMUs, then installation can be performed, but the process is very time consuming and inaccurate due to inherent inaccuracies associated with human visual image inspection
Solution Approach 1:
The patent replaces the manual mechanical/visual inspection process with an automated computer vision system. The system uses captured images to automatically detect container features (corners, edges, surfaces) and calculate optimal camera orientation angles, eliminating human visual inspection and providing both speed and precision.
Solution Approach 2:
The system enables self-service orientation by automatically processing images and computing orientation parameters without human intervention. The computer vision algorithm independently identifies container geometry and determines camera positioning, making the system self-sufficient in the orientation task.
2Reliability
If direct 3D matching technique is employed to match target point cloud to 3D template point cloud, then orientation can be determined, but the technique is not robust, lacks stable and repeatable results, is sensitive to partial structures, and involves high computation complexity
Solution Approach 1:
The patent segments the container detection task into distinct geometric feature identification steps (corners, edges, surfaces) rather than attempting full 3D point cloud matching. This segmentation simplifies computation while improving reliability by focusing on distinctive, easily identifiable features that are less sensitive to partial occlusions.
Solution Approach 2:
The system extracts only the essential geometric features (corners, edges, surfaces) needed for orientation determination from the full 3D point cloud, rather than processing the entire point cloud structure. This extraction approach reduces computational complexity while maintaining orientation accuracy.
3Reliability
If point cloud clustering technique is used, then 3D data can be segmented, but the technique is not robust, lacks stable and repeatable results, is sensitive to noise (loaders/personnel moving through loading area) and small object interference (package being moved), and creates incorrect clustering results
Solution Approach 1:
The patent applies local quality by focusing detection on specific geometric features (corners, edges, surfaces) with distinct visual characteristics rather than attempting to cluster all points uniformly. This localized feature detection approach makes the system robust to noise and interference by relying on stable geometric properties that remain identifiable despite background activity.
Data Source
AI summary
Three-dimensional (3D) depth imaging systems and methods are disclosed for assessing an orientation with respect to a container. A 3D-depth camera captures 3D image data of a shipping container. A container feature assessment application determines a representative container point cloud and (a) converts the 3D image data into 2D depth image data; (b) compares the 2D depth image data to one or more template image data; (c) performs segmentation to extract 3D point cloud features; (d) determines exterior features of the shipping container and assesses the exterior features using an exterior features metric; (e) determines interior features of the shipping container and assesses the interior features using an interior features metric; and (f) generates an orientation adjustment instruction for indicating to an operator to orient the 3D-depth camera in a second direction for use during a shipping container loading session, wherein the second direction is different than the first direction.