Road Surface Point-Cloud Cloth Simulation for Unevenness Detection
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Solution Overview
Problem
Existing systems struggle to accurately detect and avoid small objects, steps, and holes while navigating, as cameras have difficulty determining the edges of uneven surfaces from images.
Innovation Solution
An electronic device uses point cloud data to simulate a virtual cloth over the surface, performing clustering and determining unevenness based on the cloth's shape and lattice points to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a camera is used to detect road surface unevenness, then the device complexity is reduced, but the measurement precision deteriorates
Solution Approach 1:
The patent introduces a virtual cloth as an intermediary model between the point cloud data and the unevenness detection process. The cloth simulation unit places a virtual cloth with predetermined tensile force over the point cloud data, and the shape of the cloth deforms according to the underlying terrain. This intermediary transformation converts complex 3D point cloud analysis into a more manageable cloth deformation problem, improving measurement precision while maintaining relatively simple device complexity
Solution Approach 2:
The patent replaces direct geometric analysis of point cloud data with a physics-based cloth simulation approach. Instead of using complex algorithms to directly analyze point cloud coordinates for unevenness, the system uses mechanical principles of cloth deformation under gravity and tensile force to indirectly reveal the terrain features, thereby improving detection accuracy
2Measurement precision
If point cloud data is processed using cloth simulation, then the measurement precision improves, but the computing time increases
Solution Approach 1:
The patent applies partial action by performing cloth simulation only on selected regions of the point cloud data rather than processing the entire dataset. The clustering unit divides the point cloud into multiple regions, and the cloth simulation is applied to identify unevenness in specific areas of interest, reducing overall computing time while maintaining detection precision in critical zones
Solution Approach 2:
The patent segments the point cloud data into multiple clusters using the clustering unit before applying cloth simulation. By dividing the large point cloud dataset into smaller, manageable clusters, the system can process each cluster independently with the cloth simulation, significantly reducing the total computing time required while preserving the accuracy of edge and unevenness detection
Data Source
AI summary
An electronic device (100) includes an acquiring unit (131) configured to acquire point cloud data corresponding to points on a road surface, a cloth simulation unit (134) configured to output a shape of a point cloud indicated by the point cloud data determined based on a shape of a virtual cloth when the virtual cloth is put on the point cloud data with a predetermined gravity, a clustering unit (135) configured to perform clustering on the point cloud based on the shape of the point cloud, and a determiner (136) configured to determine unevenness of the clustered point cloud. A cloth simulation unit (134) determines a shape of the point cloud based on a distance between a first cloth lattice point when the virtual cloth is put on the point cloud data from a first direction and a second cloth lattice point when the virtual cloth is put on the point cloud data from a second direction. A clustering unit (135) performs clustering on the point cloud other than the points determined to be the road surface. A determiner (136) determines unevenness of the point cloud based on the number of unevenness determinations assigned to the clustered point cloud.