Point Cloud Mapping of Physical Spaces for Accurate Blueprint Updates
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Solution Overview
Problem
Traditional mapping of physical spaces, such as warehouses, is time-consuming and prone to human error, leading to inaccurate and outdated blueprints that do not readily adapt to daily changes.
Innovation Solution
Utilizing point cloud data generated from image analysis, which can be processed to accurately map physical spaces by filtering, aligning, and classifying points to identify specific structures, enabling real-time and automated updates of blueprints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual scanning techniques (LIDAR) are used to generate physical space mappings, then mapping accuracy and detail can be achieved, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent uses image copies from cameras to create point cloud representations of physical spaces, replacing the need for direct LIDAR scanning. Multiple image copies from different camera positions are processed to generate accurate 3D mappings, achieving LIDAR-level precision through photographic documentation rather than direct measurement
Solution Approach 2:
The patent substitutes mechanical LIDAR scanning systems with optical camera-based imaging systems. Instead of using mechanical laser rangefinders, the system uses cameras to capture images that are then converted into point cloud data, replacing a mechanical measurement system with an optical documentation system that is faster and less expensive
2Loss of information
If manual mapping processes are used, then detailed physical space documentation can be created, but human errors and omissions occur
Solution Approach 1:
The system performs self-verification by automatically processing images into point clouds and comparing the generated mappings against the source imagery. The computational process itself validates the completeness and accuracy of the mapping, eliminating reliance on human verification and reducing both omissions and errors
Solution Approach 2:
The system implements feedback loops where the generated point cloud data is continuously compared against the original camera images to verify accuracy. This automated feedback mechanism ensures that all physical features are captured and correctly represented, preventing both information loss and erroneous mappings
3Measurement precision
If traditional scanning equipment (LIDAR) is deployed, then accurate 3D data can be captured, but the cost and complexity of the system increases
Solution Approach 1:
The patent makes the mapping system universal by using standard cameras that can be deployed in various configurations (fixed mounts, mobile devices, drones) rather than specialized LIDAR equipment. The same camera hardware serves multiple functions including image capture, 3D reconstruction, and change detection, simplifying the overall system while maintaining accuracy
Solution Approach 2:
The system replaces expensive, complex LIDAR scanners with inexpensive camera systems. Cameras are far cheaper than LIDAR units and can be easily replaced or upgraded, reducing both the initial investment and long-term costs while maintaining sufficient measurement precision for most mapping applications
4Reliability
If manual updates are performed whenever physical spaces change, then blueprints remain current, but the time and human resources required increase significantly
Solution Approach 1:
The system enables continuous automated monitoring by periodically capturing images and regenerating point cloud mappings without interruption. This continuous process ensures blueprints are always current while eliminating the need for manual intervention, maintaining reliability without sacrificing productivity
Solution Approach 2:
The mapping system updates itself automatically by comparing new camera images against existing point cloud data and generating updated blueprints without human involvement. The system performs self-updating, ensuring current accuracy while freeing human resources for other productive tasks
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.