Mobile 3D Package Dimensioning in Complex Shipping Environments

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Solution Overview

Problem

Existing methods for determining shipping package dimensions are often inaccurate and inefficient, particularly in complex environments, affecting shipping costs and customer satisfaction.

Innovation Solution

A mobile device-based system using neural networks and depth sensors, including RGB cameras and SLAM technology, to estimate and segment object dimensions, with optional label verification and tracking, enabling precise and user-friendly dimensioning.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional manual measurement methods are used, then device complexity is low, but measurement precision and productivity are poor

Engineering Contradiction:

Improvepackage dimension measurement accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical measurement with an automated computer vision system using mobile devices. The system uses cameras, depth sensors, and neural networks to automatically measure package dimensions, eliminating the need for manual tape measures and human measurers, thereby improving precision while accepting increased system complexity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an intermediary processing layer using neural networks and point cloud algorithms. These intermediaries process raw camera images and depth data to generate accurate dimension measurements, acting as a bridge between simple capture and precise measurement without requiring direct complex mechanical measurement systems

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If automated dimensioning systems are deployed, then productivity increases, but measurement precision may deteriorate in complex environments

Engineering Contradiction:

Improvedimensioning speed and efficiencyVSAvoiddimension accuracy in complex environments

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent employs dynamic adaptive algorithms that adjust measurement parameters based on real-time environmental conditions. The system dynamically selects appropriate depth sensors, adjusts neural network processing parameters, and adapts to varying lighting and background conditions to maintain high precision while operating at high speed in complex environments

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system incorporates feedback mechanisms where measured dimensions and detected features are continuously validated against expected values. The neural networks receive feedback about measurement confidence levels and environmental conditions, allowing the system to self-correct and maintain precision while operating automatically at high productivity levels

Inventive Principle:

Principle #23Feedback

3Measurement precision

If multiple sensors are integrated into mobile devices, then measurement precision improves, but device complexity increases

Engineering Contradiction:

Improvedepth estimation accuracyVSAvoidsensor integration complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent makes mobile devices universal measurement platforms by integrating multiple sensor types (cameras, depth sensors, LiDAR) that can serve various measurement functions. These sensors are designed to work together through a unified software architecture using neural networks and point cloud processing, allowing the same device to handle different package types and environments without requiring specialized equipment for each scenario

Inventive Principle:

Principle #6Universality (Multi-functionality)

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

Provides accurate and efficient package dimensioning, supporting real-time feedback and integration with logistics systems, enhancing shipping cost calculation and inventory management.

Implementation Method 1

capturing, by a mobile device camera, an image of an object

Methodology Applied

Scientific EffectPhotography: Photography

Implementation Method 2

estimating, by a neural network, monocular depth information from the captured image

Methodology Applied

Scientific EffectMonocular depth estimation:

Implementation Method 3

generating a point cloud representation of the object based on the monocular depth information

Methodology Applied

Scientific EffectPoint cloud generation:

Implementation Method 4

estimating, by a visual simultaneous localization and mapping system, dimensions of the object based on the device's movement around the object

Methodology Applied

Scientific EffectSimultaneous localization and mapping (SLAM):

Implementation Method 5

Such sensors may include a stereo camera, a time-of-flight sensor, a structured light sensor, a LiDAR module

Methodology Applied

Scientific EffectTime-of-flight: Time of Flight

Implementation Method 6

a LiDAR module, or any combination thereof

Methodology Applied

Scientific EffectLiDAR: LIDAR

Data Source

PatentUS20250363655A1Package dimensioning

Publication Date: 2025.11.27 POSITION IMAGING INC

AI summary

A device, system, and method for dimensioning objects comprise capturing, by an optical sensor of each of one or more mobile devices, one or more images of an area containing an object; estimating the three-dimensional representation of the captured images using depth estimation techniques; identifying the object within the captured images; and calculating dimensions of the object based on the three-dimensional representation.