LIDAR-AI Landscape 3D Modeling with Layered Utility Mapping
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Solution Overview
Problem
Existing technologies lack efficient systems for accurately visualizing and navigating 3D models of homes and landscapes, recommending object placement, and mapping utility lines and commercial inventories, which are crucial for home remodeling, landscape design, and inventory management.
Innovation Solution
Utilizing LIDAR technology and AI to generate precise 3D models of homes and landscapes, recommend object placement, and map utility lines and commercial inventories by analyzing LIDAR data to measure dimensions, integrate machine learning algorithms for object recommendation, and overlay utility line locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR technology is used to capture detailed 3D data of landscapes and homes, then measurement precision and model accuracy are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent employs intermediate processing layers including point cloud generation, mesh creation, and hierarchical data structures to bridge the gap between raw LIDAR data and usable 3D models. These intermediaries break down the complex data transformation process into manageable stages, reducing system complexity while maintaining measurement precision.
Solution Approach 2:
The system segments the landscape and home data into distinct components (vegetation, structures, utility lines, interior spaces) that can be processed and visualized independently. This segmentation allows the complex LIDAR data to be divided into manageable portions, reducing overall system complexity while preserving detailed measurement accuracy for each component.
2Loss of information
If comprehensive LIDAR scanning is performed to capture all landscape features, utility lines, and home interiors, then measurement completeness is improved, but loss of time and processing duration increase
Solution Approach 1:
The system performs preliminary classification and filtering of LIDAR points during data acquisition, organizing points by type (vegetation, structure, utility line) and location before full processing. This preliminary action reduces the computational burden during subsequent processing stages, maintaining data completeness while reducing overall processing time.
Solution Approach 2:
The patent implements dynamic processing that adapts to the specific characteristics of each scanned environment. The system adjusts processing intensity and detail levels based on the complexity of the landscape features, density of utility lines, and interior space configurations, optimizing the balance between data completeness and processing time for each unique scenario.
3Loss of information
If detailed 3D models with multiple layers (vegetation, structures, utility lines) are generated, then information accuracy is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent divides the 3D model into distinct semantic layers including vegetation, structures, and utility lines, each processed and stored separately. This segmentation maintains high information accuracy for each feature type while reducing computational complexity by allowing independent processing and optimization of each layer without affecting the others.
Solution Approach 2:
The system adds a semantic classification dimension to the traditional 3D spatial data, organizing points not only by their physical location but also by their categorical identity (tree, building, power line). This additional dimension enables more efficient processing and querying while preserving comprehensive information accuracy across all feature types.
4Ease of operation
If AI algorithms are integrated for object placement recommendations and navigation, then ease of operation is improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent implements AI algorithms that provide recommendations only when requested by users, rather than continuously processing and displaying all possible information. This partial action approach maintains ease of operation by providing on-demand guidance while significantly reducing energy consumption compared to continuous AI processing.
Solution Approach 2:
The system allows users to navigate and interact with the 3D model using intuitive gestures and commands without requiring constant AI intervention. The AI serves as a supplemental tool that activates based on user needs, enabling the system to maintain simplicity and ease of use while minimizing energy-consuming computational processes.
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 3D models and navigation systems, enhances inventory management, and optimizes object placement, reducing time and cost in remodeling and inventory tasks.
Implementation Method 1
LIDAR is a technology that measures distance to a target by illuminating the target (e.g., using laser light) and then measuring the reflected light with a sensor (e.g., measuring the time of flight from the laser signal source to its return to the sensor)
Data Source
AI summary
The following relates generally to light detection and ranging (LIDAR) and artificial intelligence (AI). In some embodiments, a system: receives LIDAR data generated from a LIDAR camera; measures a plurality of dimensions of a landscape based upon processor analysis of the LIDAR data; builds a 3D model of the landscape based upon the measured plurality of dimensions, the 3D model including: (i) a structure, and (ii) a vegetation; and displays a representation of the 3D model.