Mobile LiDAR Scanner Trajectory Reconstruction
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Solution Overview
Problem
Mobile LIDAR systems often lose or ignore trajectory data during processing and archiving, leading to errors in geometric computations due to the use of IMU trajectory instead of scanner trajectory, which are not physically co-located.
Innovation Solution
An efficient method to reconstruct the scanner trajectory from mobile LIDAR point cloud data using minimal user parameters, involving spin rate estimation, scan plane estimation, scan origin estimation, and global smoothing, allowing for the reconstruction of the scanner's position and orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If trajectory data is lost or ignored during processing and archiving, then data storage and processing becomes simpler, but geometric computation accuracy deteriorates due to use of IMU trajectory instead of scanner trajectory
Solution Approach 1:
The patent creates a copy of the trajectory data by reconstructing the scanner trajectory from point cloud data. This reconstructed trajectory serves as a replacement for the lost or ignored original trajectory information, enabling accurate geometric computations without requiring the actual trajectory files to be stored or processed.
Solution Approach 2:
The patent introduces an intermediary approach by using point cloud data as a mediator to reconstruct trajectory information. Instead of directly using IMU trajectory (which has errors) or storing original trajectory data (which increases complexity), the system uses point cloud data as an intermediate to compute and reconstruct accurate scanner trajectory.
2Ease of operation
If IMU trajectory is used instead of scanner trajectory, then processing becomes easier since trajectory data is not required, but measurement accuracy deteriorates because IMU and scanner are not physically co-located
Solution Approach 1:
The patent enables the system to self-correct by reconstructing the scanner trajectory using only point cloud data that is already available during processing. The system serves itself by extracting trajectory information from the point cloud data itself, eliminating the need for external trajectory files or IMU data without requiring additional hardware or complex processing pipelines.
3Productivity
If trajectory data is not provided with mobile LIDAR point cloud, then data transmission and archiving becomes more efficient, but availability of trajectory information for advanced processing deteriorates
Solution Approach 1:
The patent extracts trajectory information from the point cloud data that is already being transmitted and archived. By taking out trajectory data from the point cloud (which is already in memory during processing), the system can reconstruct accurate scanner trajectory without requiring separate trajectory files to be transmitted or stored, thus maintaining transmission efficiency while recovering lost information.
Data Source
AI summary
An efficient approach to reconstruct the scanner trajectory of the mobile LIDAR system from the point cloud with minimal user parameters. The process of reconstructing trajectory comprises four steps: (1) spin rate estimation, (2) scan plane estimation, (3) scan origin estimation, and (4) global smoothing.