3D Scan Alignment Using Feature-Based SLAM Registration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current environment scanning systems face challenges in automatic registration of 3D scans, requiring manual intervention and resulting in inefficiencies, such as incomplete scans, high costs, and delayed processing, especially in time-sensitive situations like crime or accident scene investigations.
Innovation Solution
A mobile scanning system equipped with processors capable of simultaneous localization and mapping (SLAM), which allows for autonomous or semi-autonomous scanning, enabling the capture and registration of 3D scans by determining displacement vectors and revising scan positions, thereby facilitating accurate and efficient scan data registration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual registration procedures are used to align 3D scans, then registration accuracy can be improved, but processing time and operational complexity increase significantly
Solution Approach 1:
The system performs automatic self-registration by computing transformation parameters between overlapping scans using feature matching algorithms. The processor autonomously aligns consecutive scans without requiring manual operator intervention, allowing the scanning system to service itself during the registration process.
Solution Approach 2:
The system pre-identifies and tracks features across multiple scans before final registration is needed. By continuously monitoring feature positions and computing incremental transformation parameters during data collection, the system prepares registration information in advance, reducing final processing time.
2Measurement precision
If manual registration procedures are used to align 3D scans, then registration accuracy can be improved, but operational complexity increases
Solution Approach 1:
The system autonomously performs feature detection, matching, and transformation computation without requiring operators to manually identify corresponding points or adjust alignment parameters. The processor automatically manages the entire registration workflow, reducing operational complexity.
Solution Approach 2:
The system replaces manual mechanical adjustment procedures with automated computational algorithms. Instead of operators physically manipulating scan data or using manual alignment tools, the system uses computer vision algorithms and mathematical transformations to achieve registration automatically.
3Reliability
If multiple scans are performed to obtain complete coverage of an environment, then scan completeness is improved, but the number of registration operations increases
Solution Approach 1:
The system continuously performs preliminary feature matching and transformation computation during the scanning process itself. As each new scan is acquired, the system immediately processes it for feature correspondence with previous scans, preparing registration data incrementally rather than accumulating all scans for batch processing.
Solution Approach 2:
The system divides the complete environment scan into multiple overlapping sub-scans that are registered incrementally. Each sub-scan is aligned with its predecessors using feature matching, and the cumulative transformation builds the complete registered model piece by piece, managing complexity through modular processing.
4Productivity
If automated scanning is performed without manual registration, then productivity increases, but measurement precision may deteriorate
Solution Approach 1:
The system replaces manual measurement and alignment procedures with automated computer vision algorithms. The processor uses feature detection and matching algorithms to automatically compute transformation parameters, achieving both high productivity through automation and high precision through sophisticated computational methods.
Solution Approach 2:
The system changes the approach from manual parameter adjustment to automated parameter computation. Instead of operators setting alignment parameters based on visual inspection, the system automatically calculates transformation parameters (rotation matrices, translation vectors) based on feature correspondence, achieving both speed and accuracy.
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
The system enables efficient and accurate registration of 3D scans without manual intervention, reducing costs and time, and allowing for real-time correction of scan positions, improving data quality and reducing the need for additional scans.
Implementation Method 1
A TOF laser scanner is a scanner in which the distance to a target point is determined based on the speed of light in air between the scanner and a target point
Implementation Method 2
The beam steering mechanism includes a first motor that steers the beam of light about a first axis by a first angle that is measured by a first angular encoder
Implementation Method 3
a triangulation system, such as a scanner, projects either a line of light (e.g., from a laser line probe) or a pattern of light (e.g., from a structured light) onto the surface. In this system, a camera is coupled to a projector in a fixed mechanical relationship. The light/pattern emitted from the projector is reflected off of the surface and detected by the camera. Since the camera and projector are arranged in a fixed relationship, the distance to the object may be determined from captured images using trigonometric principles.
Data Source
Figure 1~2
Figure 3~4
Figure 5~6
AI summary
A method for performing a simultaneous location and mapping of a scanner device in a surrounding environment includes capturing a scan-data of a portion of a map of the surrounding environment. The scan-data comprises a point cloud. Further, at runtime, a user-interface is used to make, a selection of a feature from the scan-data, and a selection of a submap that was previously captured. The submap includes the same feature. The method further includes determining a first scan position as a present position of the scanner device, and determining a second scan position as a position of the scanner device. The method further includes determining a displacement vector for the map based on the first and the second scan positions. Further, a revised first scan position is computed based on the second scan position and the displacement vector. The scan-data is registered using the revised first scan position.