Mobile 3D Scanner Registration Across Multi-Level Buildings
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Solution Overview
Problem
Existing portable 3D coordinate measurement devices face challenges in efficiently capturing and registering data from multiple positions, leading to inaccuracies and inefficiencies due to the need for manual registration and external control networks, as well as limitations in capturing data in complex environments like multi-level architectures.
Innovation Solution
A mobile 3D measuring system equipped with a LIDAR sensor, orientation sensors (gyroscope, accelerometer, magnetometer), and processing units that continuously transmit data to a computing system for real-time processing, enabling the generation of accurate 3D point clouds and maps by compensating for drifting errors and optimizing data capture through loop closures and constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual registration procedures are used to align scans from different positions, then registration accuracy can be improved, but the time required and operational complexity increase significantly
Solution Approach 1:
The system performs automatic self-registration by using the scanner's own previously captured data as reference. The registration process is autonomous, utilizing feature matching and transformation algorithms to align new scans with the existing point cloud without external intervention, thereby eliminating manual operations while maintaining accuracy.
Solution Approach 2:
The system implements feedback mechanisms where registration results are continuously evaluated and refined. The registration module uses iterative optimization to minimize misalignment errors, and the system provides real-time feedback on registration quality to enable automatic adjustment of registration parameters and methods.
2Reliability
If external control networks are established for registration, then measurement reliability improves, but device complexity and setup time increase
Solution Approach 1:
The system extracts and eliminates the dependency on external control networks by implementing internal self-registration capabilities. The registration function is taken out from the external infrastructure realm and integrated into the scanner itself, using only the scanner's own captured data and computational algorithms to achieve reliable registration without external references.
Solution Approach 2:
The scanner is designed with multi-functionality, serving both as a data capture device and as a self-registering system. The same hardware and software resources are used for both scanning and registration tasks, eliminating the need for separate external control network equipment and simplifying the overall system architecture.
3Area of stationary object
If multiple stationary scans are collected and registered, then coverage of large areas improves, but productivity decreases due to repeated setup and registration
Solution Approach 1:
The system transitions from static stationary scanning to dynamic mobile scanning. The scanner is mounted on a mobile platform that moves continuously through the environment, capturing data in real-time. The registration process dynamically updates the point cloud as new data arrives, eliminating the need for repeated setup and registration cycles associated with stationary scanning.
Solution Approach 2:
The scanning and registration processes operate continuously without interruption. As the mobile platform moves through the environment, the scanner continuously captures data and the registration module continuously integrates new scans into the growing point cloud, maintaining an uninterrupted workflow that significantly improves productivity compared to discrete stationary scanning sessions.
4Adaptability or versatility
If portable scanners are used to move between positions, then adaptability to complex environments improves, but measurement precision deteriorates due to motion and positioning errors
Solution Approach 1:
The system introduces an intermediary registration process that mediates between the mobile scanner's position and the final coordinate system. The registration module acts as a computational intermediary that corrects positioning errors by matching features across multiple scans and calculating transformation parameters, thereby compensating for motion-induced inaccuracies and delivering precise coordinates despite mobile operation.
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 solution enhances the speed and accuracy of data capture, reduces manual effort, and improves the quality of 3D maps by enabling continuous data collection and real-time processing, effectively addressing the limitations of existing systems in complex environments.
Implementation Method 1
A TOF laser scanner steers a beam of light to a non-cooperative target... A distance meter in the device measures the distance to the object... A TOF laser scanner (or simply TOF scanner) is a scanner in which the distance to a target point is determined based on the speed of light in the air between the scanner and a target point
Implementation Method 2
Phase shift laser scanners determine the distance to the object by the phase shift between the outgoing and returning signal (i.e., calculating the 'shift' or 'displacement' of the reflective wave compared to the outgoing wave)
Implementation Method 3
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 (or another angle transducer). The beam steering mechanism also includes a second motor that steers the beam of light about a second axis by a second angle that is measured by a second angular encoder (or another angle transducer)
Implementation Method 4
an orientation sensor configured to estimate an altitude of the 3D measuring device
Data Source
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AI summary
A mobile three-dimensional (3D) measuring system includes a 3D measuring device (120) to capture 3D data in a multi-level architecture (e.g., a building), and an orientation sensor to estimate an altitude. One or more processing units coupled with the 3D measuring device and the orientation sensor perform a method that includes receiving a first portion of the 3D data captured by the 3D measuring device. The method further includes determining a level index based on the altitude. The level index indicates a level of the multi-level architecture at which the first portion is captured. The level index is associated with the first portion. Further, a map of the multi-level architecture is generated using the first portion, the generating comprises registering the first portion with a second portion of the 3D data responsive to the level index of the first portion being equal to the level index of the second portion. A "constraint" refers to a part/point in the environment that is scanned multiple times, and hence can be used as a reference. Aspects of the technical solutions facilitate differentiating between single-level constraints (i.e., similarities that are physically located on the same level/floor) and multi-level constraints (i.e., similarities among portions of physically different levels/floors). If similar portions are found and captured on the same floor, these are registered with each other. An operator carries a scanner (120) which includes two sensors (122, 126) mounted on supporting mounts (2).