Point Cloud Skeleton Determination via Iterative Central Points
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Solution Overview
Problem
Current methods for determining the central point of point cloud data in automotive systems are computationally complex and not suitable for real-time applications, particularly when dealing with irregular shapes and mixed environments.
Innovation Solution
A method that determines a skeleton of point cloud data by selecting starting agent positions and coordinate systems within a bounding box, performing agent moving operations parallel to coordinate axes, and iteratively calculating central points, which reduces computational complexity and adapts to specific applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing techniques or artificial neural networks are used to determine the skeleton of point cloud data, then measurement precision is improved, but device complexity and computational complexity increase significantly
Solution Approach 1:
The method segments the problem of skeleton determination into multiple simpler sub-problems: (1) determining a bounding box of the point cloud data, (2) selecting multiple coordinate systems relative to the bounding box, and (3) iteratively determining central points in each coordinate system. This segmentation reduces computational complexity while maintaining measurement precision by breaking down the complex skeleton extraction into manageable steps that can be processed efficiently.
Solution Approach 2:
The method transforms the problem from a direct complex skeleton extraction in 3D space to a series of simpler 2D cross-sectional analyses. By selecting multiple coordinate systems and determining central points in each 2D plane, the approach reduces the dimensionality of each computational step while reconstructing the full 3D skeleton through aggregation of results from multiple dimensions.
2Measurement precision
If computationally complex algorithms are used for real-time skeleton determination, then measurement precision is improved, but productivity decreases due to processing time requirements
Solution Approach 1:
The method performs preliminary actions by first determining the bounding box of the point cloud data and selecting optimal coordinate systems before conducting the actual central point determination. This preliminary setup optimizes the computational path by pre-defining the search space and coordinate frameworks, thereby reducing the computational burden during real-time processing while maintaining high measurement precision.
3Ease of operation
If center of gravity method is used to locate central points, then ease of operation is improved, but manufacturing precision deteriorates when objects have irregular shapes or attached segments
Solution Approach 1:
The method addresses the asymmetry problem in irregularly shaped objects by determining multiple central points in different coordinate systems rather than relying on a single center of gravity calculation. By analyzing the point cloud from multiple asymmetric coordinate perspectives, the method captures the true geometric center more accurately even when the object has irregular shapes or attached segments that would skew a traditional center of gravity calculation.
Data Source
AI summary
One aspect relates to a computer-implemented method for determining a skeleton of point cloud data in an automotive system for monitoring the environment of a vehicle. In order to determine the skeleton, a method of determining a central point of a point cloud data is performed multiple times, each time using different values for one or more starting parameters. The method may yield different central points depending on the selected values for the one or more starting parameters. The resulting plurality of central points is interpreted as the skeleton of the point cloud data.