Laser Scanner Image-Based Scan Settings for 3D Point Cloud Accuracy
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Solution Overview
Problem
Existing terrestrial laser scanners face challenges with high data volume processing, registration errors due to moving objects and reflective surfaces, and inconsistent color information due to light conditions, leading to inefficient and inaccurate 3D scanning.
Innovation Solution
A terrestrial laser scanner with a camera and machine learning algorithm to categorize scan regions based on image significance, dynamically adjusting scan parameters such as density, speed, and radiation intensity to improve data collection efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high scan density is used to capture all object features, then measurement precision is improved, but data volume increases and processing time increases
Solution Approach 1:
The patent applies different scan densities to different regions of interest. The system identifies significant objects and features in the scene, then dynamically adjusts scan density to be higher for regions containing these features and lower for background areas. This resolves the contradiction by maintaining high measurement precision where needed while reducing overall data volume and processing time.
Solution Approach 2:
The patent implements dynamic scan parameter adjustment based on real-time scene analysis. The system continuously monitors the measurement scene, identifies changes in object significance, and adapts scan density accordingly. This dynamic approach allows the system to optimize between precision and processing time based on the actual content being scanned.
2Measurement precision
If high scan density is used to capture all object features, then measurement precision is improved, but data volume increases
Solution Approach 1:
The system applies local quality by varying scan density across different spatial regions. High scan density is concentrated on identified objects and features of interest, while peripheral and background regions receive lower scan density. This approach maintains measurement precision for important features while significantly reducing the total data volume generated.
Solution Approach 2:
The patent implements partial action by selectively scanning only the portions of the scene that contain significant features. Rather than uniformly scanning the entire field of view at high density, the system performs partial scans focused on relevant areas, reducing overall data volume while maintaining precision where it matters.
3Productivity
If scanning continues through moving objects, then productivity is improved, but registration accuracy deteriorates
Solution Approach 1:
The system uses feedback from scene monitoring to detect moving objects and adjusts scanning parameters in response. When motion is detected, the system can pause scanning, alert the operator, or automatically adjust parameters to account for the moving object. This feedback mechanism maintains registration accuracy without significantly impacting overall productivity.
Solution Approach 2:
The patent implements dynamic scanning that adapts to scene changes in real-time. The system continuously monitors for moving objects and dynamically adjusts scan parameters, timing, and trajectory to accommodate motion. This allows the system to maintain high productivity while preserving registration accuracy through adaptive parameter changes.
4Device complexity
If fixed scan parameters are used, then device complexity is reduced, but adaptability to different scenes deteriorates
Solution Approach 1:
The system implements self-service by automatically analyzing the measurement scene and selecting appropriate scan parameters without requiring manual configuration. The automated scene analysis and parameter selection capabilities allow the system to adapt to different scenes independently, maintaining low operational complexity while achieving high adaptability.
Solution Approach 2:
The patent employs dynamic parameter adjustment that automatically adapts scan settings to the specific scene being measured. The system analyzes scene characteristics such as object distribution, lighting conditions, and feature density, then dynamically modifies scan parameters accordingly. This maintains simple device operation while achieving versatile scene adaptability.
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
Enhances scanning efficiency by reducing unnecessary data points, minimizing registration errors, and improving color consistency, resulting in a more accurate and optimized 3D point cloud generation.
Implementation Method 1
at least one radiation source or emitter for generating optical measurement radiation, often laser radiation
Implementation Method 2
optical means, including a rotating deflector, by means of which the generated measuring radiation can be emitted in free space onto a target or object to be measured
Implementation Method 3
Distance determination is based on reflected measurement radiation, reflected from the irradiated target object so that at least a part of the measuring radiation is reflected back to the laser scanner and detected
Implementation Method 4
at least one camera for capturing 2D-images with an optical camera axis in known spatial relationship to the scanning direction
Data Source
Figure 1a~1b
Figure 2a~2b
AI summary
A stationary terrestrial laser scanner and method with image processing, based on a machine learning algorithm, of a 2D-image of a scan sphere, captured with a camera of the stationed laser scanner before measurement of scan points, in such a way that the image is partitioned in multiple clusters of different predefined categories of measurement significance and setting at least one adaptable scan parameter according to the presence and/or absence of clusters of a significance category in the first 2D-image.