Vehicular Cargo Management via 3D Plane Clustering
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Solution Overview
Problem
Cargo vehicles face inefficiencies in utilizing their space, leading to suboptimal cargo management and difficulty in locating specific parcels during unloading, which affects logistics and transportation efficiency.
Innovation Solution
A system utilizing cameras to capture and analyze the cargo space, identifying a Region of Interest, extracting planes based on geometry, clustering similar planes, and modeling a multi-dimensional bounding box to accurately determine the position of objects within the cargo space, enabling efficient space utilization and parcel location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual cargo space management is used, then device complexity is reduced, but cargo space utilization efficiency deteriorates
Solution Approach 1:
The patent replaces manual mechanical cargo management with an automated computer vision system using cameras and machine learning algorithms to detect, track, and manage cargo positions automatically, eliminating the need for manual tracking while improving space utilization efficiency
Solution Approach 2:
The system creates digital copies of cargo items through image capture and processing, generating virtual representations that are easier to manage, track, and analyze than physical cargo, enabling efficient space management through digital twins of physical objects
2Quantity of substance
If cargo space is densely packed to maximize utilization, then cargo capacity increases, but time to locate specific parcels increases
Solution Approach 1:
The system continuously captures images of cargo space and provides real-time feedback on parcel positions through the bounding box identification algorithm, enabling delivery executives to quickly locate specific parcels by receiving updated position information without manually searching through densely packed cargo
Solution Approach 2:
The patent introduces an intermediary system (camera-based detection and image processing algorithm) that mediates between the densely packed cargo and the delivery executive, translating physical cargo positions into easily interpretable digital information that speeds up parcel location
3Measurement precision
If detailed tracking of each parcel position is implemented, then cargo management precision improves, but data processing complexity increases
Solution Approach 1:
The patent segments the cargo space into discrete regions and represents each parcel as an independent bounding box with specific coordinates, dividing the complex task of tracking entire cargo manifests into manageable individual object detections that can be processed separately and efficiently
Solution Approach 2:
The system transforms complex cargo position data into simplified parametric representations using bounding box coordinates (x, y, z positions and dimensions), changing the data format from complex unstructured information to standardized parameters that are easier to store, process, and query
Data Source
AI summary
A method for identifying a position of an object in a cargo space includes identifying a region of interest (ROI) from a field of view of the cargo space being captured by a camera, where the camera captures at least one of depth and color of the field of view, extracting a plurality of planes from the ROI, where the plurality of planes correspond to a geometry of the object, clustering similar and nearby planes, where the clustering is based on a weight of two or more planes of the plurality of planes and where the weight is assigned based on a property of orthogonality and a property of dimensions of two or more planes of the plurality of planes, modelling a multi-dimensional bounding box corresponding to the object based on the clustered planes, and identifying a position of the object based on a position of the multi-dimensional bounding box.