Linear Object Point Cloud Extraction from Sparse Data
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Solution Overview
Problem
Existing technologies face challenges in extracting a point cloud of a linear object such as a cable when the point cloud data is sparse, due to insufficient acquisition of point cloud data or interference from obstacles.
Innovation Solution
The method involves acquiring point cloud data and movement track coordinates, then repeatedly counting the number of point clouds within a certain distance from a point cloud on the movement track to extract a point cloud of a linear object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional clustering methods are used to create scan lines from point cloud data, then three-dimensional models can be created when point cloud data is sufficiently acquired, but the point cloud of linear objects cannot be extracted when point cloud data is sparse or insufficient
Solution Approach 1:
The patent applies preliminary action by performing movement track acquisition and point cloud data acquisition before the extraction process. The system pre-processes the data by acquiring the movement track of the measurement platform and organizing point cloud data according to this track, which enables subsequent extraction operations to succeed even with sparse point cloud data by establishing a structural framework in advance
Solution Approach 2:
The patent applies segmentation by dividing the point cloud data processing into distinct segments: (1) acquiring point cloud data and movement track separately, (2) dividing point cloud data into multiple groups based on movement track, (3) extracting linear objects from each group, and (4) synthesizing results. This segmentation allows the system to handle sparse data more effectively by processing it in manageable portions rather than requiring complete data upfront
2Measurement precision
If point cloud data acquisition is insufficient due to small number of outputs or obstacles, then traditional methods cannot extract cable portions, but the new method enables extraction by repeatedly counting point clouds within certain distance
Solution Approach 1:
The patent applies feedback by implementing an iterative counting process where the system repeatedly counts the number of point clouds within a certain distance from points on the movement track. This feedback mechanism allows the system to refine its extraction by continuously checking and adjusting based on the counted points, enabling accurate extraction even when the overall point cloud density is low
Solution Approach 2:
The patent applies partial action by focusing the extraction process on specific regions near the movement track rather than requiring complete point cloud coverage. By concentrating computational effort on counting points within certain distances from the track, the system achieves precise extraction of linear objects using only a partial subset of available point cloud data
Data Source
AI summary
An object of the present disclosure is to enable extraction of a point cloud of a linear object such as a cable having a sparse point cloud.The present disclosure is a device and a method in which point cloud data representing three-dimensional coordinates and a movement track at the time of measuring the point cloud data are acquired, and the number of point clouds within a certain distance DN from the point cloud on the movement track is repeatedly counted to extract a point cloud of a linear object such as a cable 92.