Point Cloud Mapping for Accurate, Updatable Warehouse Blueprints
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Solution Overview
Problem
Traditional mapping techniques for physical spaces, such as warehouses, are time-consuming, expensive, and prone to human error, leading to inaccurate and outdated blueprints that fail to reflect daily changes.
Innovation Solution
Utilizing point cloud data generated from 3D scans and images captured by cameras, which are processed to create accurate, real-time, and dynamically updated blueprints of physical spaces by identifying and classifying objects like vertical poles, racks, and pallets using filtering and machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual scanning techniques are used to generate physical space mappings, then measurement precision can be achieved, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent replaces manual mechanical scanning operations with automated computer vision systems. Multiple cameras capture images simultaneously, and image processing algorithms automatically generate point clouds and mappings, eliminating the need for manual navigation and scanning while maintaining measurement precision.
Solution Approach 2:
The system performs preliminary actions by pre-positioning multiple cameras to capture the entire physical space in a single operation. Image processing and point cloud generation are performed in advance, allowing mappings to be created before manual methods would complete the scanning process.
2Loss of information
If manual mapping methods are employed, then detailed blueprints can be created, but human error reduces reliability
Solution Approach 1:
The system performs self-service through automated image processing algorithms that objectively identify and map physical features without human intervention. The computer vision system processes images, generates point clouds, and creates mappings autonomously, eliminating human error while maintaining complete detail capture.
Solution Approach 2:
The system incorporates feedback mechanisms where processed images and generated point clouds are continuously refined through algorithmic analysis. The mapping process includes validation steps that automatically correct errors and ensure accuracy, providing feedback loops that improve reliability without sacrificing detail.
3Measurement precision
If traditional scanning equipment is used, then accurate 3D data can be obtained, but the cost and complexity of the system increases
Solution Approach 1:
The patent employs multiple standard cameras instead of expensive specialized scanning equipment. These conventional imaging devices are more readily available, less complex, and sufficient for generating accurate point clouds when used in arrays, reducing both cost and system complexity while maintaining measurement precision.
Solution Approach 2:
The system uses universal camera devices that can serve multiple functions - capturing images for mapping, measuring distances, and identifying physical features. This multi-functionality eliminates the need for specialized scanning equipment, reducing device complexity while maintaining accurate 3D data acquisition.
4Adaptability or versatility
If manual updates are performed on blueprints, then changes in physical space can be reflected, but the process requires continuous human effort and time
Solution Approach 1:
The system enables continuous automatic updates by continuously capturing images with the camera array and processing them through the mapping algorithms. This continuous automated operation maintains current mappings of the physical space without requiring periodic manual updates, improving productivity while maintaining adaptability to changes.
Solution Approach 2:
The mapping system performs self-updates through automated image processing and point cloud regeneration. When changes occur in the physical space, the system automatically detects them through new image captures and updates the mappings without human intervention, maintaining adaptability while eliminating the need for continuous human effort.
Data Source
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AI summary
This specification describes systems and methods for generating a mapping of a physical space from point cloud data for the physical space. The methods can include receiving the point cloud data for the physical space, filtering the point cloud data to, at least, remove sparse points from the point cloud data, aligning the point cloud data along x, y, and z dimensions that correspond to an orientation of the physical space, and classifying the points in the point cloud data as corresponding to one or more types of physical surfaces. The methods can also include identifying specific physical structures in the physical space based, at least in part, on classifications for the points in the point cloud data, and generating the mapping of the physical space to identify the specific physical structures and corresponding contours for the specific physical structures within the orientation of the physical space.