Trailer Utilization 3D Point Cloud Segmentation
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Solution Overview
Problem
Traditional imaging systems in the commercial freight industry struggle to accurately determine trailer utilization, especially when freight is loaded chaotically and non-uniformly, leading to inefficient loading and potential delays.
Innovation Solution
A method and system that capture 3D image data of trailers, segment the data into regions, apply a utilization algorithm to determine matching points, and calculate normalized heights to create a 3D model visualization of trailer utilization, effectively addressing the challenges of chaotic and non-uniform loading conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional imaging systems are used to calculate trailer utilization, then the system is simple to operate, but the measurement precision deteriorates when freight is arranged chaotically and non-uniformly
Solution Approach 1:
The trailer interior is divided into multiple regions (front, middle, rear sections) to systematically analyze chaotic freight arrangements. Each region is processed independently through the utilization algorithm, enabling accurate measurement of utilization metrics even when freight is non-uniformly distributed throughout the trailer.
Solution Approach 2:
The system transitions from traditional 2D imaging to 3D point cloud analysis, adding a depth dimension to the measurement. This dimensional enhancement allows the algorithm to accurately calculate utilization metrics for chaotically arranged freight by analyzing spatial distribution in three dimensions rather than flat projections.
2Reliability
If traditional loading assessment methods are used, then the process is quick, but the reliability of loading metrics deteriorates leading to inefficient loading
Solution Approach 1:
The system performs preliminary 3D scanning and point cloud generation during the loading process itself, rather than requiring separate assessment steps. This preliminary capture of spatial data enables reliable utilization calculations to be made available in real-time, improving both reliability and reducing time loss by eliminating post-loading assessment delays.
Solution Approach 2:
The patent replaces manual or mechanical loading assessment methods with an automated optical sensing system using ToF sensors. This substitution eliminates human error and manual measurement delays, providing reliable loading metrics automatically through electronic data processing rather than mechanical or manual evaluation.
3Measurement precision
If comprehensive 3D analysis is performed on the entire trailer, then the measurement precision improves, but the computational complexity increases
Solution Approach 1:
The trailer's 3D space is segmented into multiple regions, allowing the utilization algorithm to process each region separately. This segmentation reduces the computational complexity of analyzing the entire trailer at once while maintaining measurement precision, as each smaller region can be independently calculated and then aggregated for the overall utilization metric.
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
The solution provides accurate and reliable assessment of trailer utilization, improving loading efficiency by identifying unused space and optimizing cargo placement, regardless of loading style or cargo shape and size.
Implementation Method 1
Time of Flight (ToF) sensors are frequently used to analyze the interior of freight containers using three-dimensional (3D) and two-dimensional (2D) image data
Data Source
AI summary
Methods for assessing container utilization are disclosed herein. An example method includes capturing an image featuring a container, and segmenting the image into a plurality of regions. For each region the example method may include cropping the image to exclude data that exceeds a respective forward distance threshold, and iterating over each data point to determine whether a matching point is included. Responsive to whether a matching point included for a respective data point, the method may include adding the respective data point or the matching point to a respective region based on a position of the respective data point. Further, the method may include calculating a normalized height of the respective region based on whether or not a gap is present in the respective region; and creating a 3D model visualization of the container that depicts container utilization.