3D Point Cloud Boundary Detection for Automated Shipping Measurement
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Solution Overview
Problem
Conventional techniques for estimating shipping costs and packaging materials are inefficient and inaccurate, requiring multiple devices and user guesswork, leading to frustration and excess costs due to inaccuracies.
Innovation Solution
A mobile device with an augmented reality module generates three-dimensional point cloud data to detect the physical object's boundary, filtering out ground plane points and using nearest neighbor search to calculate a two-dimensional boundary, which is then extruded to form a three-dimensional boundary for accurate shipping data generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional techniques using tape measure and manual estimation are used, then device complexity is reduced, but measurement precision and productivity deteriorate
Solution Approach 1:
The patent replaces manual mechanical measurement tools (tape measure, ruler) with an automated optical system using a camera and image processing algorithms. The system captures images of the object and automatically calculates dimensions through coordinate transformations and boundary detection, eliminating the need for physical contact measurement tools and significantly improving measurement precision.
Solution Approach 2:
The system enables self-service measurement where the object itself provides the measurement information through its visual appearance in captured images. The automated image processing system extracts dimensional data directly from the object's image without requiring user intervention or manual tool usage, making the measurement process independent and accurate.
2Device complexity
If conventional techniques requiring multiple devices and user intervention are used, then device complexity is reduced, but productivity deteriorates
Solution Approach 1:
The patent merges multiple measurement functions into a single integrated system. The camera captures images, the processor automatically performs coordinate transformations, calculates dimensions, and generates measurement results all in one workflow. This consolidation eliminates the need for multiple separate devices and manual operations, significantly improving productivity while maintaining simplicity.
Solution Approach 2:
The system performs preliminary image capture and processing actions automatically before user input is needed. The camera captures the object image, the system pre-processes the image data, and prepares measurement calculations in advance, reducing the overall measurement time and improving productivity by eliminating sequential manual steps.
3Measurement precision
If automated image processing is used, then measurement precision and productivity improve, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical measurement systems with a software-based image processing solution. The automated coordinate transformation algorithms and boundary detection methods perform precise measurements through computational geometry rather than physical measurement tools, achieving high precision while using a simple camera-based hardware setup.
Data Source
AI summary
Physical object boundary detection techniques and systems are described. In one example, an augmented reality module generates three dimensional point cloud data. This data describes depths at respective points within a physical environment that includes the physical object. A physical object boundary detection module is then employed to filter the point cloud data by removing points that correspond to a ground plane. The module then performs a nearest neighbor search to locate a subset of the points within the filtered point cloud data that correspond to the physical object. Based on this subset, the module projects the subset of points onto the ground plane to generate a two-dimensional boundary. The two-dimensional boundary is then extruded based on a height determined from a point having a maximum distance from the ground plane from the filtered cloud point data.