Terrain Data Generation With Adaptive Point Density for Slope Features
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Solution Overview
Problem
Existing systems struggle to accurately reproduce geographical features like shoulders or toes of slopes due to uniform mesh spacing, leading to inaccuracies in terrain data generation.
Innovation Solution
A construction history calculation system that uses a controller to detect the posture of a work machine, sets configuration surfaces, and calculates construction history data with a predetermined point density to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform mesh spacing is used for terrain data generation, then the system complexity is reduced and processing is simplified, but the accuracy of geographical features such as slope shoulders or toes deteriorates
Solution Approach 1:
The patent applies local quality by transitioning from uniform mesh spacing to variable point density distribution. Recording points are strategically placed with higher density in areas containing geographical features such as slope shoulders or toes, while maintaining lower density in flat areas. This allows the system to maintain simplicity overall while achieving high accuracy locally where it matters most.
Solution Approach 2:
The patent implements dynamics by making the point density adaptive rather than static. The system dynamically adjusts the density of recording points based on the detected characteristics of the terrain. Areas with significant elevation changes or geographical features automatically receive higher point density, while uniform areas use lower density, allowing the system to respond to terrain variability.
2Measurement precision
If higher point density is used to improve terrain data accuracy, then the measurement precision of geographical features is improved, but the quantity of data and processing requirements increase
Solution Approach 1:
The patent applies local quality by concentrating recording points only where geographical features are detected. Instead of uniformly distributing high-density points across the entire work area, the system identifies specific locations containing slope shoulders, toes, or other significant features and places recording points selectively in those areas. This maintains high measurement precision where needed while minimizing the total quantity of data collected.
3Ease of operation
If equal interval meshes are used for work area representation, then the ease of operation and processing is improved, but the ability to accurately reproduce characteristic terrain features deteriorates
Solution Approach 1:
The patent applies local quality by making the point distribution adaptive to terrain characteristics. Rather than using equal interval meshes that treat all areas uniformly, the system varies the spacing and density of recording points based on local terrain features. Areas with slope shoulders or toes receive concentrated point density for accurate reproduction, while flat areas use sparser distribution, maintaining ease of processing overall.
Data Source
AI summary
The construction history calculation system includes a controller that calculates construction history data based on the detection results of a posture detection device that detects the posture of a work machine. The controller acquires target surface data and, based on the acquired target surface data, sets multiple configuration surfaces, including the target configuration surface and adjacent configuration surfaces that constitute the target surface data, as surface areas. It sets multiple record points to achieve a predetermined point density for the surface areas, calculates the trajectory of the work machine's working device based on the detection results of the posture detection device, and calculates the position information of the working device's trajectory, associating the position information of the monitor points of the working device that constitute the trajectory with each of the multiple record points, as construction history data.