VIS-Guided Laser Scanner Positioning for Point Cloud Registration
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Solution Overview
Problem
Current methods for surveying measurement surroundings with mobile measuring devices, such as laser scanners, often require multiple deployments and post-processing to ensure complete data acquisition, which can be time-consuming and inefficient, especially when dealing with complex or partially obstructed environments, and may result in missed regions or registration issues.
Innovation Solution
A method utilizing a Visual Inertial System (VIS) to automatically capture and track object features in multiple measurement regions, allowing for real-time comparison and registration of point clouds during deployment changes, enabling immediate identification of suitable deployment positions and minimizing the number of necessary scans by guiding the user to optimal positions for gap-free surveying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple deployments are performed to ensure complete data acquisition, then measurement completeness is improved, but time expenditure and complexity increase
Solution Approach 1:
The system performs preliminary assessment of deployment suitability by analyzing VIS data and comparing it with previously captured measurement surroundings before the actual scanning deployment. This preliminary action identifies optimal positions in advance, ensuring measurement completeness while minimizing the number of actual deployments needed, thus reducing time expenditure.
Solution Approach 2:
The system provides real-time feedback to the user about the suitability of current or planned deployment positions by comparing VIS-captured features with reference trajectories. This feedback mechanism guides the user to optimal positions, ensuring complete data acquisition while minimizing unnecessary deployments and time expenditure.
2Reliability
If multiple deployments are performed to cover all measurement regions, then survey completeness is improved, but device deployment complexity increases
Solution Approach 1:
The system automatically assesses deployment suitability and generates guidance information without requiring complex user analysis or decision-making. The automated comparison of VIS data with reference trajectories and the generation of deployment recommendations simplify the user's task while ensuring survey completeness, effectively making the system self-service in nature.
3Measurement precision
If real-time comparison of measurement surroundings is performed during deployment change, then registration accuracy is improved, but processing requirements increase
Solution Approach 1:
The system extracts only the essential VIS data (object features, positions, orientations) from the full measurement surroundings for comparison purposes. By focusing on key features rather than processing entire point clouds in real-time, the system achieves accurate registration while minimizing processing requirements and energy consumption.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for real-time pre-registration of laser scans, reducing costly rework and enabling immediate data completeness checks on-site, optimizing the surveying process by minimizing scan positions and time expenditure while ensuring geometric accuracy and homogeneous point density.
Implementation Method 1
automatically optically captured and tracked using a visual inertial system (VIS)
Data Source
AI summary
A method for ascertaining a suitable deployment of a mobile measuring device within measurement surroundings, wherein first and second measurement surroundings containing first and second object features are automatically optically captured at the first deployment and tracked using a visual inertial system (VIS) and within the scope of changing the deployment. The first and second measurement surroundings are compared, wherein the comparison is based on searching for corresponding first and second object features visible in a certain number and quality in the first and second measurement surroundings, wherein this certain number and quality of corresponding features is a criterion that a registration of the first and second point cloud is possible.


