Visibility Model Using Adaptive Distance Fields
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Solution Overview
Problem
Current methods for identifying visibility of targets in an environment are inefficient, requiring excessive time, computational resources, and do not produce continuous signed visibility functions, making it difficult to determine optimal sensor placement for unobstructed lines of sight.
Innovation Solution
A computer system uses adaptive distance fields and visibility functions to form volumes in an environment, determining whether locations are on, outside, or inside surfaces, and calculating minimum values along lines of sight to identify visibility, allowing for efficient sensor placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to identify visibility of targets, then visibility information can be obtained, but excessive time and computational resources are required
Solution Approach 1:
The environment is divided into discrete volumes with vertices, where visibility is calculated at each vertex. This segmentation allows the complex continuous visibility problem to be broken into manageable discrete units, reducing computational complexity while maintaining accuracy.
Solution Approach 2:
The patent pre-calculates and stores visibility values at volume vertices before actual sensor placement decisions are made. This preliminary computation creates a visibility model that can be quickly queried during sensor deployment, avoiding repeated complex calculations.
2Measurement precision
If traditional visibility calculation methods are used, then target visibility can be determined, but excessive computational resources are consumed
Solution Approach 1:
The continuous environment is segmented into volumetric elements with discrete vertices. Visibility is computed only at these vertex points rather than continuously throughout space, significantly reducing the number of calculations required while preserving essential visibility information.
Solution Approach 2:
The patent creates a simplified computational model (visibility model) that copies the essential visibility characteristics of the real environment. This model uses discrete volume vertices and pre-computed visibility values, serving as a lightweight representation that consumes fewer computational resources than detailed physical simulations.
3Ease of operation
If sensor placement is made without continuous signed visibility functions, then sensor deployment can proceed, but optimal placement for unobstructed lines of sight cannot be determined
Solution Approach 1:
The patent transforms visibility information into a continuous signed function where positive values indicate visible regions and negative values indicate obscured regions. This parameter transformation enables gradient-based optimization methods to automatically determine optimal sensor placements that maximize unobstructed lines of sight.
Solution Approach 2:
The continuous signed visibility function provides immediate feedback about the quality of any potential sensor location. By evaluating the function value at candidate positions, the system can automatically identify optimal placements without manual trial-and-error, combining ease of operation with placement precision.
Data Source
AI summary
A method and apparatus for identifying visibility of targets. A first function is selected to indicate whether locations in an environment are at a surface for objects in the environment, outside the surface, or inside the surface. First volumes are formed for the environment. Each volume in the first volumes has a size selected such that a difference between first interpolated values for each volume and first values generated using the first function for each volume is within a threshold. Second volumes are formed for the environment. Each of the second volumes has a size selected such that a difference between second interpolated values for each volume and second values generated using a second function for each volume is within a threshold. The second values are minimum values along lines from each volume in the second number of volumes to a target in the environment.