Laser Scanner Top Surface Estimation for Hoisting Loads
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Solution Overview
Problem
Conventional techniques for creating three-dimensional maps from point cloud data acquired by laser scanners attached to airplanes face challenges in accurately estimating top surfaces of measurement target objects, especially when objects are close together, requiring multiple measurements and being burdensome in data acquisition and complex in calculation.
Innovation Solution
A method for estimating the top surface of a measurement target object using point cloud data acquired from above, involving data processing to group the data into layers, calculate elevation differences, detect overlaps, and combine planes to estimate the top surface efficiently and accurately, without relying on statistical methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional three-dimensional point cloud analysis methods using principal component analysis are used to determine top surfaces, then comprehensive three-dimensional shape analysis is achieved, but computational complexity increases and processing time extends
Solution Approach 1:
The point cloud data is divided into multiple layers based on elevation values, with each layer representing a specific height range. This segmentation allows independent processing of each layer to identify top surface points, reducing the computational burden compared to analyzing all points simultaneously through principal component analysis.
Solution Approach 2:
The method extracts only the points that constitute the top surface by filtering point cloud data layer by layer. By taking out and processing only the relevant top-layer points rather than performing comprehensive three-dimensional analysis on all points, the calculation complexity is significantly reduced while maintaining accuracy.
2Measurement precision
If multiple measurements are performed to accurately capture top surfaces of closely spaced objects, then measurement accuracy improves, but data acquisition time and operational burden increase
Solution Approach 1:
The method performs preliminary layering of point cloud data by elevation values before detailed top surface analysis. This preliminary organization of data into distinct layers enables accurate identification of top surfaces in a single measurement pass, eliminating the need for multiple repeated measurements to resolve closely spaced objects.
Solution Approach 2:
The approach introduces a vertical dimension (elevation-based layering) to distinguish between closely spaced objects that may overlap in horizontal views. By organizing points into multiple elevation layers, the method can accurately separate and identify top surfaces of adjacent objects from a single measurement, avoiding the need for multiple measurement angles or positions.
3Reliability
If comprehensive point cloud data processing is performed to ensure accurate top surface estimation, then measurement reliability improves, but processing speed decreases
Solution Approach 1:
Point cloud data is segmented into multiple elevation-based layers, allowing parallel or sequential processing of each layer independently. This segmentation maintains measurement reliability by ensuring each layer is thoroughly analyzed while improving overall processing speed by dividing the computational workload into manageable units.
Solution Approach 2:
The method performs processing on partial sets of points (only those in each elevation layer) rather than processing all points simultaneously. This partial action approach maintains the reliability needed for accurate top surface estimation while significantly improving processing throughput compared to comprehensive simultaneous analysis of all point cloud data.
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
Enables real-time estimation of top surfaces with reduced computational burden and improved accuracy, allowing for efficient processing of point cloud data from laser scanners without the need for complex statistical methods.
Implementation Method 1
point cloud data of the measurement target object acquired by a laser scanner
Data Source
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AI summary
The present invention estimates the top surface of a measurement target on the basis of a data point group that corresponds to the top surface of a measurement target and is obtained using a laser scanner. This top-surface estimation method for hoisting loads W and objects C is provided with: a data point group acquisition step in which a laser scanner 62 acquires data point groups P in a hoisting load region WA which includes a hoisting load W and an object C from above the hoisting load W and the object C; a group allocation step in which a data processing unit 70 divides the hoisting load region WA into layers which constitute a plurality of groups which have a prescribed thickness d in the vertical direction, and allocates the acquired data point groups P to the plurality of layer groups; and a top-surface estimation step in which the data processing unit 70 estimates the top surfaces of the hoisting load W and the object C in each layer group on the basis of the data point groups P allocated to the plurality of layer groups.