LIDAR Point Cloud Edge Detection Using Scan Pattern Grids
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Solution Overview
Problem
Existing methods for point cloud feature extraction and segmentation in LIDAR data face challenges such as inefficient data structuring, unreliable normal estimation, significant information loss due to down-sampling, inconsistencies in colormetric data, and the need for extensive radiometric calibration, particularly when dealing with large volumes of unevenly distributed data and varying spatial distributions.
Innovation Solution
The approach involves structuring point cloud data into a scan pattern grid, performing edge detection using normal variation analysis independent of normal vectors, and applying region growing techniques to segment the data, thereby simplifying neighbor definitions and improving computational efficiency while maintaining detailed information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If point cloud data is organized using a tree data structure (e.g., k-D tree, octree), then neighbor searching and visualization can be performed effectively, but the depth of the tree structure varies dramatically due to unevenly distributed point cloud, resulting in significantly increased computational complexity
Solution Approach 1:
The patent segments the point cloud data into multiple 2-D scan pattern grids based on the acquisition geometry, where each grid corresponds to a specific scan angle range. This segmentation approach avoids the need for complex hierarchical tree structures by organizing data into flat, angle-based grids that are easier to traverse and search, thereby reducing computational complexity while maintaining neighbor searching efficiency
Solution Approach 2:
The patent transforms the traditional 3-D spatial organization into a 2-D angular grid structure based on scan patterns. By organizing points according to their angular coordinates (azimuth and elevation angles) rather than spatial coordinates, the system creates a dimensionality-reduced data structure that simplifies neighbor searching and reduces tree depth variation, thereby lowering computational complexity
2Stability of the object's composition
If a grid structure is pre-defined for organizing point cloud data, then data can be structured systematically, but the user has to balance cell size/detail with computation time
Solution Approach 1:
The patent changes the organizational parameters of the grid structure by using angular coordinates (scan angles) as the basis for grid cell definition instead of fixed spatial dimensions. This parameter change allows the grid to adapt naturally to the LIDAR scan pattern, providing systematic organization without requiring manual balancing of cell size and computation time, as the angular bins are inherently optimized for the acquisition geometry
3Productivity
If down-sampling or re-sampling is applied to reduce data volume in grid structures, then computation time can be reduced, but significant information loss occurs or memory is wasted due to large number of cells with no data
Solution Approach 1:
The patent employs a self-service mechanism where the angular grid structure automatically adapts to the actual data distribution by creating bins only where scan lines exist. Each grid cell corresponds to a specific angular range that naturally contains data points, eliminating the need for down-sampling while avoiding creation of empty cells. This self-adapting structure maintains all original information and reduces computation time by only processing populated angular bins
4Measurement precision
If normal vectors are estimated for each point to perform edge detection, then intersection edges can be detected, but the normal estimation becomes unreliable at edges or rough surfaces
Solution Approach 1:
The patent extracts the edge detection process from the normal vector computation by directly analyzing the geometric relationships between neighboring points in the angular grid. Instead of computing normal vectors for each point and then detecting edges based on normal variations, the method extracts edge information directly from the spatial configuration of points within and between angular bins, thereby achieving reliable edge detection without the unreliability of normal estimation at edges or rough surfaces
Data Source
AI summary
A computer implemented scheme for a light detection and ranging (LIDAR) system where point cloud feature extraction and segmentation by efficiently is achieved by: (1) data structuring; (2) edge detection; and (3) region growing.