Handheld 3D Scanner Object Dimension Estimation
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Solution Overview
Problem
Existing 3D cameras struggle to accurately distinguish and measure the dimensions of a primary object when multiple objects are present in the field of view, leading to inefficiencies in estimating dimensions or volume, especially in transportation and shipping contexts.
Innovation Solution
A handheld device equipped with a 3D camera and electro-optical reader captures a 3D point cloud, clusters data points, and uses a minimum volume bounding box to isolate and orient the primary object's dimensions, ensuring accurate measurement by fitting a bounding box with specific facial constraints relative to the base plane.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a 3D camera captures all objects in the field of view, then dimension data is obtained for all objects, but the primary object cannot be distinguished from secondary objects leading to measurement errors
Solution Approach 1:
The patent segments the captured point cloud data into multiple data clusters, where each cluster represents a separate object. By dividing the scene into distinct object groups, the system can identify and measure the primary object independently from secondary objects, resolving the contradiction between capturing all objects and accurately identifying the primary object.
Solution Approach 2:
The patent introduces an intermediary reference point (such as a code or marker) associated with the primary object. This reference point serves as a mediator that links the visual data from the 3D camera with the identification system, enabling the system to distinguish the primary object from secondary objects and accurately measure its dimensions.
2Measurement precision
If manual object selection is used to identify the primary object, then measurement accuracy is maintained, but processing time increases significantly
Solution Approach 1:
The patent performs preliminary actions by automatically clustering objects and identifying the primary object based on pre-established criteria (such as code detection, object size, or position) before the measurement process begins. This preliminary automatic identification eliminates the need for manual selection during operation, maintaining measurement accuracy while significantly reducing processing time.
Solution Approach 2:
The system enables self-service by automatically identifying and selecting the primary object without human intervention. The algorithm autonomously processes the point cloud data, clusters objects, identifies the primary object based on predefined criteria, and proceeds with measurement, making the system self-sufficient and eliminating time-consuming manual operations.
3Quantity of substance
If all objects in the field of view are measured, then complete dimension data is obtained, but processing complexity and time increase
Solution Approach 1:
The patent extracts only the necessary information by identifying and isolating the primary object from the complete set of captured objects. Instead of processing dimension data for all objects, the system extracts and processes only the primary object's data based on identification criteria, maintaining data completeness for the target object while significantly reducing processing complexity.
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 enables rapid, efficient, and accurate estimation of the dimensions of a main object even when secondary objects are nearby, improving measurement precision and reducing processing time.
Implementation Method 1
a scanner, e.g., an electro-optical reader, scans the scene over a field of view to obtain a position of a reference point of a code associated with the object and reads the code
Implementation Method 2
a dimensioning sensor, e.g., a three-dimensional (3D) camera, captures, in automatic response to the reading of the code, a three-dimensional (3D) point cloud of data points of the scene
Data Source
AI summary
Dimensions of an object associated with an electro-optically readable code are estimated by aiming a handheld device at a scene containing the object supported on a base surface. A scanner on the device scans the scene over a field of view to obtain a position of a reference point of the code associated with the object, and reads the code. A dimensioning sensor on the device captures a three-dimensional (3D) point cloud of data points of the scene in automatic response to the reading of the code. A controller clusters the point cloud into data clusters, locates the reference point of the code in one of the data clusters, extracts from the point cloud the data points of the one data cluster belonging to the object, and processes the extracted data points belonging to the object to estimate the dimensions of the object.