LiDAR Aerial Matching Case Study
Introduction
The objective of the project is the flightline matching process of Aerial LiDAR point cloud.
Key Features
- Automatic classification of LiDAR point cloud.
- Solve hrpm for whole data set.
- Solve z for individual flightline.
Methodology
Processing steps
- Import trajectories into terrascan
- Split any trajectories which overlap themselves
- Import time stamped laser point into terrascan
- Automatically classify low points, buildings and ground for each flightline.
- Run find match tool and solve for heading, roll, pitch and mirror scale corrections for the whole data set
- Apply the correction to the laser data
- Run find match tool and solve for elevations corrections for the individual flightline
- Apply the correction to the laser data
- Check the flightlines visually in cross sections to determine if the correction was good.
- Try solving parameters with different settings to get improved result .
Quality assurance
Expertise with stringent knowledge in matching will be allocated to assess the quality of the data. Quality checks will be carried according to checklist prepared with reference to acceptance criteria.
Deliverables
