Point Cloud Deduplication via Occupancy Grid Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Point cloud data sets generated by range sensors often contain duplicate points, which increase processing volume and impact modeling algorithms, as the sensors may pause or re-scan areas, leading to inefficient data processing.
Innovation Solution
A computer-implemented method that uses an occupancy grid to identify and remove duplicate points by mapping points to locations and maintaining a predetermined number of unique points, discarding duplicates and marking locations as occupied or unoccupied based on the number of recent points processed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all data points from the range sensor are processed, then complete three-dimensional modeling is achieved, but processing time and computational resources increase significantly due to duplicate points
Solution Approach 1:
The patent extracts and removes duplicate data points from the point cloud dataset by comparing each point's coordinates with previously processed points. This extraction of unnecessary duplicate information reduces the dataset size while preserving all unique spatial information needed for accurate three-dimensional modeling, thereby decreasing processing time without compromising model completeness
Solution Approach 2:
The patent performs preliminary filtering of duplicate points before the main three-dimensional modeling process. By pre-processing the point cloud data to eliminate redundancies upfront, the system prepares a optimized dataset that requires less computational effort during subsequent modeling operations, thus reducing overall processing time while maintaining model accuracy
2Reliability
If all data points including duplicates are used for modeling, then comprehensive surface coverage is achieved, but memory usage and computational load increase
Solution Approach 1:
The patent extracts only the unique data points from the point cloud by comparing coordinates and identifying duplicates. This extraction process removes redundant information while preserving complete surface coverage, reducing the data set volume from potentially millions of points to a minimized unique set that maintains all necessary geometric information for accurate modeling
Solution Approach 2:
The patent changes the parameter of data set composition by transforming a redundant collection of points into a streamlined unique set. By applying coordinate comparison and deduplication algorithms, the system alters the data structure to contain only necessary unique points, reducing memory consumption and computational load while maintaining complete surface representation
3Reliability
If duplicate points are retained in the data set, then original data integrity is maintained, but processing efficiency and algorithm performance deteriorate
Solution Approach 1:
The patent performs preliminary deduplication processing before the main modeling algorithms execute. By pre-identifying and removing duplicate points while preserving unique spatial information, the system maintains data integrity of all unique features while significantly improving processing efficiency during subsequent modeling operations, as algorithms operate on a minimized unique dataset rather than redundant full dataset
Data Source
AI summary
Systems and methods for reducing a point cloud data set are provided. According to aspects of the present disclosure, a method includes receiving a point of a point cloud data set, the point having three-dimensional coordinates. The point's coordinates are mapped to a location to determine whether a different point's coordinates have already been mapped to the location. The point is discarded when a different point's coordinates have been mapped to the location.