Mobile Scanner Drift Correction Using Pre-Existing Maps
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Solution Overview
Problem
Mobile scanning devices, including those mounted on movable structures, suffer from inaccuracies due to error accumulation known as drift, leading to incomplete or distorted 3D models of environments, especially when scanning large areas like hallways where objects block light beams, resulting in shadows and incomplete data.
Innovation Solution
A system that uses a mobile scanning platform equipped with a light source, image sensor, and processors to measure coordinate values, which receives pre-existing data such as CAD models or golden point clouds to correct current scan data by registering it into a single frame of reference, eliminating drift and providing accurate environmental models without the need for loop closure techniques or targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mobile scanning devices are used to scan large environments, then scanning coverage and productivity are improved, but measurement precision deteriorates due to drift accumulation
Solution Approach 1:
The system performs preliminary actions by capturing images and identifying features before drift correction is applied. The current scan data is registered to the reference model using identified features, and only after this registration is the drift correction applied to adjust the coordinates. This ensures that the geometric relationships are established before correcting for accumulated errors.
Solution Approach 2:
The system uses feedback from the reference model (previously generated map) to correct the current scan data. By comparing features in the current scan with corresponding features in the reference model, the system calculates drift corrections and applies them to improve coordinate accuracy while maintaining the benefits of mobile scanning.
2Loss of information
If multiple scans are performed to obtain complete environmental data, then measurement completeness is improved, but loss of time increases due to multiple registration operations
Solution Approach 1:
The system performs preliminary identification of features and their correspondence between current and reference scans before the actual drift correction and registration. By pre-identifying matching features (walls, corners, objects) and their locations in both the current scan and reference model, the system streamlines the subsequent registration process and reduces overall processing time.
3Measurement precision
If drift correction using reference models is implemented, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system uses an intermediary reference model (previously generated map or CAD model) to mediate between the current mobile scan and the true environment geometry. This reference model serves as a bridge that contains the geometric relationships and feature locations, allowing the system to correct drift without requiring direct comparison between multiple current scans or complex real-time calculations.
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 enhances data quality by accurately reflecting the environment, reducing errors caused by drift, and allowing for continuous scanning without the requirement of additional reference points, resulting in more complete and accurate 3D models.
Implementation Method 1
transmitting a beam of light onto the objects and collecting the reflected or scattered light to determine the distance, two-angles (i.e., an azimuth and a zenith angle), and optionally a gray-scale value
Implementation Method 2
transmitting a beam of light onto the objects and collecting the reflected or scattered light
Implementation Method 3
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
Data Source
AI summary
A system and method for measuring coordinate values of an environment is provided. The system includes a coordinate measurement scanner that includes a light source that steers a beam of light to illuminate object points in the environment, and an image sensor arranged to receive light reflected from the object points to determine coordinates of the object points in the environment. The system also includes one or more processors for performing a method that includes receiving a previously generated map of the environment and causing the scanner to measure a plurality of coordinate values as the scanner is moved through the environment, the coordinate values forming a point cloud. The plurality of coordinate values are registered with the previously generated map into a single frame of reference. A current map of the environment is generated based at least in part on the previously generated map and the point cloud.