Point Cloud Elimination for High-Resolution Lidar Processing
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Solution Overview
Problem
Existing methods for reducing point cloud data processing load do not effectively address how to eliminate point clouds without altering the shape of the circumscribing rectangle, especially when high-resolution Lidar systems increase the number of point clouds.
Innovation Solution
A processing apparatus that eliminates point cloud data by removing points that are next to each other with a distance difference smaller than a threshold, while keeping points that are essential for maintaining the shape of the circumscribing rectangle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution Lidar is used to acquire more point cloud data, then measurement precision and object detection accuracy are improved, but processing load increases and real-time recognition cannot be performed
Solution Approach 1:
The patent extracts and removes redundant point cloud data that does not contribute to object detection accuracy. By eliminating points with small distance differences (smaller than a threshold) while preserving points that define the circumscribing rectangle shape, the system reduces processing load while maintaining measurement precision for real-time recognition
Solution Approach 2:
The patent changes the parameter of point cloud density by selectively removing points based on distance difference thresholds. This parameter adjustment reduces the total number of points processed while preserving the essential geometric features needed for accurate object detection, thereby improving processing speed without sacrificing measurement precision
2Productivity
If point cloud data is reduced by making coordinate point density equal to or lower than a threshold, then processing load is reduced, but object shape recognition accuracy deteriorates
Solution Approach 1:
The patent applies local quality by treating different regions of the point cloud differently. Points that are critical for defining the circumscribing rectangle shape are preserved with higher density, while redundant points in other regions are removed. This selective approach maintains object shape recognition accuracy while reducing overall processing load
Solution Approach 2:
The patent performs preliminary action by pre-identifying and preserving points that are essential for maintaining the circumscribing rectangle shape before eliminating redundant points. This ensures that the essential geometric features are protected during the data reduction process, preventing deterioration of shape recognition accuracy
Data Source
AI summary
Provided are a processing apparatus and a point cloud elimination method that can suppress point cloud processing load even if the number of points included in point clouds increases due to realization of high resolution of an apparatus that acquires point clouds of an object. The processing apparatus includes a memory (storage apparatus) and a processor. The memory stores data of a point cloud of an object. The processor eliminates data of a second point (subject point) that is included in the point cloud and is next to a first point (previous point) that is included in the point cloud from the data of the point cloud when the difference between the distance to the first point and the distance to the second point is smaller than a threshold (first threshold Th1) (Step 1903: YES) (Step 1908).