Dynamic Cargo Volume Measurement Using Wheel Reference Point
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing dimensioning systems for cargo in freight and less-than-truckload cross docking environments are prone to errors due to manual measurement methods and require modifications to vehicles, which is impractical for large facilities with diverse fleets, and are unreliable in varying lighting conditions and color similarities between cargo and vehicles.
Innovation Solution
A method that uses three-dimensional scanner data to create a model of the vehicle and cargo, identifying a point of reference on the wheel to separate vehicle and cargo models without requiring modifications or color assumptions, allowing for accurate volume determination while the cargo is being transported.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual measurement methods are used for dimensioning cargo, then operational simplicity is maintained, but measurement precision and reliability deteriorate due to human error
Solution Approach 1:
The patent replaces manual mechanical measurement methods with an automated optical measurement system using laser scanners. The system captures three-dimensional data of cargo and vehicle automatically, eliminating human involvement in the measurement process while maintaining high precision through computational analysis of the scanned point cloud data.
Solution Approach 2:
The system enables self-service dimensioning by automatically capturing cargo dimensions through laser scanning and performing computational separation of cargo from vehicle components. The process requires no human intervention for measurement taking or data processing, as the system independently identifies and measures cargo volume by subtracting vehicle point cloud data.
2Ease of manufacture
If color-based separation methods are used to distinguish cargo from vehicle, then ease of implementation is improved, but reliability deteriorates under varying lighting conditions and color similarities
Solution Approach 1:
The patent changes the measurement parameter from color-based identification to geometric and spatial parameter-based identification. Instead of relying on color properties that vary with lighting conditions, the system uses the three-dimensional spatial coordinates and geometric characteristics of scanned points to distinguish cargo from vehicle components, ensuring reliable separation independent of lighting conditions.
Solution Approach 2:
The patent introduces a computational intermediary process that separates cargo from vehicle by analyzing spatial relationships in the point cloud data. Rather than directly using color information, the system uses the known vehicle geometry as a reference model and computationally subtracts it from the combined vehicle-cargo scan, leaving only the cargo dimensions. This intermediary computational step eliminates reliance on color properties.
3Measurement precision
If vehicle modifications are required for dimensioning systems, then measurement capability is improved, but adaptability deteriorates for diverse vehicle fleets
Solution Approach 1:
The patent creates a universal dimensioning system that can measure cargo on any vehicle type without requiring vehicle-specific modifications. The system uses a reference point on the vehicle (such as a wheel hub) as a consistent origin for all measurements, and the computational method adapts to different vehicle geometries by scanning and subtracting the specific vehicle's point cloud data. This allows the same system to work across diverse fleets of forklifts and transport vehicles.
Solution Approach 2:
The system employs dynamic adaptation by capturing the actual vehicle geometry through laser scanning and using that scanned data as the reference model for subtraction. Rather than requiring pre-programmed knowledge of each vehicle type, the system dynamically generates the vehicle reference model during operation, allowing it to adapt to any vehicle configuration without modifications or pre-registration.
4Measurement precision
If complex separation algorithms are used to distinguish cargo from vehicle, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the measurement process into distinct phases: first capturing the combined vehicle-cargo point cloud, then separating the vehicle reference model, and finally calculating the cargo volume by subtracting the vehicle points from the combined points. This segmentation breaks down the complex separation task into manageable computational steps, reducing overall system complexity while maintaining high precision through systematic processing.
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 method provides a robust and efficient way to determine cargo volume accurately without vehicle modifications, working across diverse fleets and conditions, reducing errors and operational complexity in large facilities.
Implementation Method 1
At discrete points in time, based on the time delay or phase shift between the emitted light and the received light, the distance travelled by the light is calculated.
Implementation Method 2
laser-ranging and laser-scanning systems
Data Source
AI summary
Cargo objects, in a freight-related environment, are dynamically dimensioned while being held at a cargo-handling position of a vehicle. A three-dimensional model is obtained comprising points representing surfaces of the vehicle. Using the model, the position of a point of reference of a first wheel of the vehicle is obtained, as is the position of a split point relative to the position of the first wheel point of reference. A driving direction of the vehicle is obtained. A splitting plane is determined, which passes through the split point and is perpendicular to the driving direction. A three-dimensional model of the cargo is determined by subtracting, from the vehicle three-dimensional model, the points that are positioned on the side of the splitting plane opposite to the side of the splitting plane that make up the first wheel point of reference. The volume is then determined from the cargo three-dimensional model.