Aligning Mobile Scanner Point Clouds Using Stationary Reference Data
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Solution Overview
Problem
Current methods for generating 3D point clouds, especially with stationary and mobile scanners, face challenges in achieving high accuracy and completeness efficiently, as they require multiple setups and suffer from low accuracy when capturing large areas or areas with obstructions, leading to time-consuming data acquisition and incomplete scans.
Innovation Solution
A method that combines data from stationary and mobile scanners by aligning and calibrating the mobile scanner's data with the stationary scanner's data using an overlap region, improving positional accuracy and correcting deformations in real-time, allowing for the generation of a complete and accurate 3D point cloud during the scanning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a stationary laser scanner is used to scan large areas or areas with obstructions, then measurement accuracy is maintained, but multiple setups are required which increases time consumption and reduces productivity
Solution Approach 1:
The scanning task is divided into two segments: a stationary laser scanner handles areas requiring high measurement accuracy, while a mobile scanner handles large areas and hard-to-reach locations. This segmentation allows each device to operate in its optimal performance range, resolving the contradiction between accuracy and productivity.
Solution Approach 2:
The patent combines data from both stationary and mobile scanners by aligning their coordinate systems and merging point clouds. This merging approach allows the system to achieve both high accuracy (from the stationary scanner) and high productivity (from the mobile scanner covering more ground faster).
2Productivity
If a mobile scanner is used to increase productivity and accessibility, then data acquisition speed improves, but measurement accuracy decreases
Solution Approach 1:
The stationary scanner acts as an intermediary reference system. The mobile scanner's data is aligned to the stationary scanner's highly accurate coordinate system, allowing the mobile scanner to maintain high productivity while achieving measurement accuracy through the intermediary reference framework.
Solution Approach 2:
Different regions of the scanned environment receive different quality treatments: areas scanned by the stationary scanner achieve high measurement accuracy, while areas scanned by the mobile scanner achieve acceptable accuracy for productivity purposes. The final combined point cloud has locally optimized quality based on the scanning device used.
3Reliability
If multiple setups of the stationary laser scanner are performed to capture complete surfaces, then completeness of the point cloud improves, but time consumption increases significantly
Solution Approach 1:
The patent introduces dynamic mobility to the scanning system. The mobile scanner can dynamically position itself to capture areas that would require multiple stationary setups, such as hard-to-reach locations and occluded surfaces. This dynamic approach achieves completeness faster by eliminating the need for multiple time-consuming stationary setups.
Solution Approach 2:
The mobile scanner performs preliminary scanning of large areas and hard-to-reach locations quickly, capturing data that would otherwise require multiple stationary setups. This preliminary action reduces the need for multiple stationary scanner setups, thereby reducing time consumption while maintaining completeness.
4Measurement precision
If the laser beam is moved slowly over the surface to increase point-to-point resolution, then measurement accuracy improves, but scanning speed decreases
Solution Approach 1:
The system applies partial high-resolution scanning only where necessary. The stationary scanner provides high point-to-point resolution for critical areas, while the mobile scanner provides sufficient (but not maximum) resolution for large areas. This partial application of high-resolution scanning maintains accuracy where needed while preserving overall scanning speed.
Data Source
AI summary
Three-dimensional (3D) point cloud generation using a stationary laser scanner and a mobile scanner. The method includes scanning a first part of a surrounding with the stationary laser scanner, obtaining a first 3D point cloud, scanning a second part of the surrounding with the mobile scanner, obtaining a second 3D point cloud, whereby there is an overlap region of the first part and the second part, and aligning the second 3D point cloud to the first 3D point cloud to form a combined 3D point cloud. The positional accuracy of points of the second 3D point cloud is increased by automatically referencing second scanner data of the overlap region, generated by the mobile scanner, to first scanner data of the overlap region, generated by the stationary laser scanner. Therewith, deformations of the second 3D point cloud and its alignment with the first 3D point cloud are corrected.