3D Point Cloud Calibration Using User-Selected Reference Points
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Solution Overview
Problem
Existing technologies face challenges in aligning multiple 3D point clouds without requiring pre-mapping processes, leading to computational resource waste and delays, and necessitate expert knowledge for setting parameters like orthogonal coordinates and rotational movements.
Innovation Solution
A method for aligning 3D point clouds using user-selected reference points from different sensors, without pre-mapping, to reduce computational resources and simplify operations, involving rotation and translation based on these points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-mapping processes and 3D maps are used for aligning point clouds, then alignment precision is improved, but computational resource consumption increases and processing delay occurs
Solution Approach 1:
The patent extracts and uses only the essential reference points from point clouds for alignment, eliminating the need for comprehensive pre-mapping processes and 3D maps. This selective extraction approach maintains alignment precision while significantly reducing computational resource consumption and processing time.
Solution Approach 2:
The patent performs preliminary selection of reference points from point clouds before the actual alignment process. By pre-identifying key reference points that will be used for alignment, the system avoids the computational burden of processing entire point clouds or using pre-mapping data, thus improving processing efficiency while maintaining alignment accuracy.
2Measurement precision
If pre-mapping processes are used for aligning point clouds, then alignment precision is improved, but computational delay increases
Solution Approach 1:
The patent extracts only the necessary reference points from point clouds, eliminating the time-consuming pre-mapping processes. This extraction approach maintains alignment precision by focusing on critical reference points while significantly reducing computational delay.
Solution Approach 2:
The patent skips the traditional pre-mapping process entirely by directly using reference points extracted from point clouds for alignment. This skipping of unnecessary intermediate steps reduces computational delay while maintaining alignment precision through direct reference point matching.
3Measurement precision
If complex parameter settings like orthogonal coordinates and rotational movements are required, then alignment precision is improved, but operational complexity increases
Solution Approach 1:
The patent enables the system to automatically determine alignment parameters and transformations based on selected reference points, eliminating the need for users to manually configure complex parameters like orthogonal coordinates and rotational movements. This self-service approach maintains alignment precision while greatly simplifying operation.
Solution Approach 2:
The patent introduces reference points as an intermediary between the raw point clouds and the alignment process. These reference points serve as simple mediators that automatically guide the alignment without requiring users to directly handle complex coordinate systems or rotational parameters, thus maintaining precision while improving ease of operation.
Data Source
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AI summary
An electronic device includes a communication circuit, an input device, at least one processor, and at least one memory. The at least one processor stores instructions, and the instructions are configured to obtain a first point cloud for a three-dimensional space from a first sensing device among a plurality of sensing devices, obtain a second point cloud for the three-dimensional space from a second sensing device among the plurality of sensing devices, select at least two first reference points from the first point cloud based on a user input, select at least two second reference points from the second point cloud based on the user input, and align the first point cloud and the second point cloud based on the at least two first reference points and the at least two second reference points.