LiDAR Point Cloud and Stereo Image Point Cloud Fusion
Abstract
The advent of Light Detection and Ranging (LiDAR) point cloud collection has significantly improved the ability to model the world in precise, fine, three-dimensional details. The objective of this research was to demonstrate accurate, foundation methods for fusing LiDAR data and photogrammetric imagery and their potential for change detection. The scope of the project was to investigate optical image to LiDAR registration methods, focusing on several dissimilar image types including Optical Bar Camera (OBC), high resolution aerial frame, and WorldView 1 satellite with varying LiDAR point densities. An innovative optical image to LiDAR data registration process was established. This approach was demonstrated for one image type using the rational polynomial coefficients (RPC) representation of the panoramic math model improving accuracy from 1.9 m to 0.5 m root mean square (RMS) error. Comparison of stereo imagery point cloud data to the LiDAR point cloud using a 90% confidence interval highlighted changes that included small scale (< 50cm), sensor dependent change and large scale, new home construction change. This research also proposed a fused LiDAR and stereo image base layer as the foundation for further LiDAR/image fusion.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2013
- Accession Number
- ADA589768
Entities
People
- Paul L. Basgall
Organizations
- Naval Postgraduate School