Automated Floor Map Generation from 360° Panorama Images
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Solution Overview
Problem
Existing methods struggle to effectively generate accurate and detailed floor maps of building interiors without physical presence, especially for remote users, as they lack efficient tools to analyze and display visual information about building layouts and structural elements.
Innovation Solution
The use of automated computing systems that analyze 360° spherical panorama images to generate floor maps, allowing users to interact and input data to define room shapes and layouts, without requiring depth-sensing equipment, enabling precise mapping of building interiors for navigation and display purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated computing systems analyze 360° spherical panorama images to generate floor maps, then mapping precision and detail are improved, but device complexity increases due to the need for advanced image processing algorithms
Solution Approach 1:
The system divides the complex task of floor map generation into multiple processing stages: panorama image acquisition, feature detection, room shape identification, layout reconstruction, and map generation. Each stage processes specific aspects independently, improving precision while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediate data structures and processing layers between raw panorama images and final floor maps. These intermediaries include extracted feature points, detected room boundaries, and spatial relationship models, which simplify the overall transformation process and enhance mapping precision.
2Productivity
If automated image analysis is used to generate floor maps without physical presence, then productivity is improved, but measurement precision may worsen due to lack of depth-sensing equipment
Solution Approach 1:
The system compensates for lack of direct depth sensing by utilizing the spherical panorama's 360-degree horizontal and vertical angular information. By mapping visual features across multiple viewpoints and using angular relationships to infer spatial dimensions, the system achieves acceptable depth accuracy through dimensional transformation from 2D images to 3D spatial understanding.
Solution Approach 2:
The patent employs preliminary feature detection and calibration steps that prepare the image data for more accurate subsequent processing. By pre-identifying room corners, walls, and structural elements in the panorama images, and establishing camera position and orientation beforehand, the system improves measurement precision in the final floor map generation.
3Loss of information
If detailed visual information about building interiors is captured, then information completeness is improved, but data processing time increases
Solution Approach 1:
The system extracts only the essential features and information needed for floor map generation from the comprehensive panorama images. By identifying and isolating key elements such as room boundaries, doors, windows, and structural features, the system maintains information completeness for mapping purposes while significantly reducing processing time by ignoring irrelevant visual data.
Solution Approach 2:
The patent implements a tiered processing approach where essential features are processed in full detail while less critical information receives reduced processing. This partial action strategy ensures that the most important spatial and structural information is captured completely, while secondary details are processed more efficiently, balancing information completeness with processing time.
Data Source
AI summary
Techniques are described for using computing devices to perform automated operations involved in analysis of images acquired in a defined area, as part of generating mapping information of the defined area for subsequent use (e.g., for controlling navigation of devices, for display on client devices in corresponding GUIs, etc.). The defined area may include an interior of a multi-room building, and the generated information including a floor map of the building, such as from an analysis of multiple 360° spherical panorama images acquired at various viewing locations within the building (e.g., using an image acquisition device with a spherical camera having one or more fisheye lenses to capture a panorama image that extends 360 degrees around a vertical axis)—the generating may be further performed without detailed information about distances from the images' viewing locations to objects in the surrounding building.