Mobile 3D Scanner Registration Across Levels With Drift Compensation
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Solution Overview
Problem
Existing portable 3D coordinate measurement devices face challenges in efficiently capturing and registering data from multiple levels and positions, leading to inaccuracies and inefficiencies due to drift errors from noisy sensors and limited processing power.
Innovation Solution
A mobile 3D measuring system equipped with LIDAR sensors, orientation sensors (gyroscope, accelerometer, magnetometer), and distributed processing capabilities, which continuously captures data and generates a 3D point cloud by determining level indices and applying transformations to compensate for drifting errors, ensuring accurate registration and mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If portable 3D measurement devices use noisy sensors for data capture, then device portability and flexibility are improved, but drift errors and measurement precision deteriorate
Solution Approach 1:
The patent introduces an intermediary optimization system that processes raw sensor data through drift compensation algorithms. This intermediary layer separates the portable sensor from the final measurement output, allowing noisy sensor data to be corrected before being used for 3D mapping, thus resolving the contradiction between portability and precision
Solution Approach 2:
The system implements feedback mechanisms where measured drift errors are continuously monitored and fed back into the registration process. This allows real-time correction of drift accumulation, maintaining measurement precision despite using portable noisy sensors
2Productivity
If portable 3D measurement devices capture data from multiple levels and positions, then coverage and productivity are improved, but data registration accuracy and reliability deteriorate due to drift errors
Solution Approach 1:
The patent segments the global optimization problem into local registration tasks for each scan position, then combines them through hierarchical optimization. This segmentation allows efficient processing of multi-level data while maintaining registration accuracy through localized drift compensation before global integration
Solution Approach 2:
The system introduces a temporal dimension to the registration process by optimizing across time sequences of scans. This allows drift errors to be compensated by analyzing patterns across multiple time points, improving registration reliability without sacrificing capture speed
3Measurement precision
If global optimization methods are applied to correct drift errors, then measurement precision and reliability are improved, but computational complexity and device complexity increase
Solution Approach 1:
The patent applies preliminary drift compensation transformations to individual scans before performing global optimization. This preliminary action reduces the magnitude of corrections needed in the final optimization step, simplifying the computational problem while maintaining precision
Solution Approach 2:
The system changes optimization parameters dynamically based on scan conditions, using simplified models for routine scans and more complex models when drift is detected. This adaptive parameter selection maintains measurement precision while reducing average computational complexity
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 data capture speed, reduces errors, and improves the quality of 3D maps by enabling real-time optimization and accurate registration of data from multiple levels and positions, addressing the limitations of existing systems.
Implementation Method 1
A TOF laser scanner steers a beam of light to a non-cooperative target, such as a diffusely scattering surface of an object. A distance meter in the device measures the distance to the object
Implementation Method 2
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 3
an orientation sensor configured to estimate an altitude of the 3D measuring system
Implementation Method 4
The mobile 3D measuring system includes LIDAR sensors, orientation sensors (gyroscope, accelerometer, magnetometer)
Data Source
AI summary
A mobile three-dimensional (3D) measuring system includes a 3D measuring device configured to capture 3D data in a multi-level architecture, and an orientation sensor configured 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.