3D Point Cloud Cargo Counting for Standardized Placement Detection
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Solution Overview
Problem
Manual counting of bulk-cargoes is time-consuming and labor-intensive, leading to low efficiency in cargo counting.
Innovation Solution
A method utilizing 3D point cloud data to determine the placement state of cargoes within a preset region, followed by calculating the quantity of cargoes based on this data, employing 3D laser scanners and point cloud stitching to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual counting method is used for bulk-cargoes, then counting can be performed without specialized equipment, but counting efficiency is low and time-consuming
Solution Approach 1:
The patent replaces manual mechanical counting with an automated optical measurement system using 3D laser scanners to capture point cloud data of cargoes. The system automatically processes the point cloud data to calculate cargo quantities, eliminating the need for manual counting and significantly improving efficiency while reducing time consumption.
2Productivity
If 3D point cloud data analysis is used for cargo counting, then counting efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments the cargo counting process into distinct modules: point cloud data acquisition using 3D laser scanners, point cloud stitching to assemble complete cargo views, placement state determination to identify standardized positions, and quantity calculation based on detected positions. This modular segmentation manages system complexity by organizing functions into separate, manageable components.
Solution Approach 2:
The patent introduces point cloud data as an intermediary representation between the physical cargo and the counting system. The 3D laser scanners convert physical cargo positions into digital point cloud data, which then serves as the basis for automated analysis and quantity calculation, simplifying the overall measurement process.
3Speed
If automated 3D point cloud analysis is implemented, then counting speed increases, but measurement precision requirements increase
Solution Approach 1:
The patent performs preliminary point cloud stitching to assemble complete cargo views before conducting placement state determination. By pre-processing the point cloud data to ensure complete and accurate cargo representations, the system prepares the data in advance for more reliable automated analysis, maintaining precision while enabling faster processing.
Solution Approach 2:
The patent implements a dynamic assessment approach where the system determines placement states based on analyzing multiple cargo positions and their relationships. The system adapts its analysis based on the detected cargo configurations, allowing flexible and accurate determination of standardized positions even with variations in cargo arrangements.
Data Source
AI summary
A method for cargo counting, a computer equipment, and a storage medium are provided in the disclosure. The method includes the following. Three-dimensional (3D) point cloud data of a set of cargoes within a preset placement region is obtained based on a cargo-counting instruction. Whether the set of cargoes are in a first placement state is determined according to the 3D point cloud data. Based on a determination that the set of cargoes are in the first placement state, a quantity of the set of cargoes is calculated.