LiDAR Elevation Estimation Using Masking Zones
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing LiDAR systems face challenges in accurately estimating the elevation of ground locations that were not directly measured by LiDAR instrumentation, as they rely on complex and resource-intensive spatial interpolation techniques.
Innovation Solution
A method that estimates the elevation at a principal point using LiDAR data from spatially related secondary points, where secondary points within a predetermined masking zone are either discarded or assigned less relevance, employing a weighted mean approach to prioritize closer points for more accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spatial interpolation is used to estimate elevation at unsampled locations, then estimation accuracy is improved, but computational complexity and resource intensity increase
Solution Approach 1:
The patent divides the set of secondary points into different groups based on their spatial relationship to the principal point. Points are segmented into those within the masking zone (excluded or given less weight) and those outside (given full weight). This segmentation reduces the effective number of points involved in the interpolation calculation, thereby reducing computational complexity while maintaining accuracy through selective use of relevant data points.
Solution Approach 2:
The patent applies different quality weights to different secondary points based on their location relative to the masking zone. Points outside the masking zone are assigned full weight, while points within the masking zone are either excluded or assigned reduced weight. This local differentiation optimizes the interpolation by emphasizing locally relevant data while reducing the influence of potentially less relevant distant points, improving accuracy without requiring all points to be processed equally.
2Loss of information
If all secondary points are used in spatial interpolation, then data utilization is maximized, but processing time and resource consumption increase
Solution Approach 1:
The patent extracts and excludes secondary points that fall within the masking zone from the interpolation calculation, or assigns them reduced weight. This extraction of irrelevant or less relevant data points reduces the number of calculations required while preserving the essential information from points outside the masking zone. The masking zone itself is extracted as a defined spatial region that determines which points are excluded.
3Area of stationary object
If distant secondary points are included in estimation, then coverage area is increased, but estimation accuracy may decrease due to reduced spatial dependence
Solution Approach 1:
The patent dynamically adjusts the weight assigned to each secondary point based on its distance from the principal point and its position relative to the masking zone. Points closer to the principal point and outside the masking zone receive higher weights, while distant points or those within the masking zone receive lower weights or are excluded. This dynamic weighting scheme allows the system to adaptively balance coverage and accuracy by emphasizing locally relevant data while still considering a broader area when appropriate.
Data Source
AI summary
Aspects of the present invention are directed at estimating the value of an attribute at a specified geographic location. In one embodiment, a method is provided that estimates the elevation at a principal point using LiDAR data that was collected from spatially related secondary points. More specifically, the method includes identifying secondary points where sample attribute data was obtained that are within a predetermined distance to the principal point where the attribute is being estimated. A secondary point may be selected and allocated a masking zone and a determination made regarding whether one or more distant secondary points are within the area of the masking zone. In this regard, more distant secondary points that are inside a masking zone may be assigned less relevance when estimating the value of the attribute.