Point Cloud Mapping for Fast, Accurate Physical Space Updates
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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physical spaces, such as warehouses, are time-consuming, expensive, and prone to human error, leading to inaccurate and outdated blueprints that fail to dynamically reflect changes in the environment.
Innovation Solution
Utilizing point cloud data generated from image analysis, which can be processed to filter noise, align, classify, and identify specific physical structures, enabling automated and real-time updates of blueprints and maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional scanning techniques (LIDAR) are used to generate physical space mappings, then measurement precision is improved, but loss of time and cost increase significantly
Solution Approach 1:
The patent replaces traditional mechanical scanning systems (LIDAR) with a camera-based image processing system. The camera captures images that are then processed through point cloud generation and filtering algorithms to create accurate physical space mappings, thereby eliminating the time-consuming mechanical scanning process while maintaining measurement precision through computational methods.
Solution Approach 2:
The patent creates a digital copy (point cloud representation) of the physical space from image data. Instead of directly measuring with LIDAR, the system captures visual information and transforms it into a point cloud model that replicates the physical space's geometry, enabling accurate mapping without the time penalty of traditional scanning.
2Adaptability or versatility
If manual mapping processes are used, then adaptability to physical spaces is improved, but productivity decreases due to time-consuming operations
Solution Approach 1:
The system performs automatic point cloud filtering and mapping generation without continuous human intervention. The computer-executable instructions autonomously process images, generate point clouds, apply filters to remove noise, and produce final mappings, transforming a manual process into an automated self-service system that maintains adaptability while dramatically improving productivity.
Solution Approach 2:
The patent applies filtering operations to point cloud data before final mapping generation. By pre-processing the point cloud to remove noise and outliers in advance, the system prepares clean data for mapping, reducing the time required for final processing while maintaining the flexibility to handle various physical space configurations.
3Reliability
If traditional mapping methods are used, then reliability of initial mapping is improved, but loss of time for updates increases when changes occur
Solution Approach 1:
The patent enables continuous or periodic remapping by capturing new images and regenerating point clouds when changes occur in the physical space. Instead of relying on static manual updates, the system maintains current mappings through continuous image capture and automated processing, ensuring reliability while minimizing update time through automation.
Solution Approach 2:
The system performs mapping updates at periodic intervals or when triggered by detected changes. By automating the periodic regeneration of point clouds and mappings, the system maintains reliable, up-to-date representations of physical spaces without the time investment required for manual remapping, allowing frequent updates at minimal cost.
Data Source
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.