Detecting Systematic Biases and GNSS/INS Drifts in LiDAR Data

Ayman Habib, Ana Paula Kersting, Ki-In Bang

Abstract:	Recent developments in GNSS/INS technology are having a positive impact on the wide adoption of LiDAR (Light Detection And Ranging) as a prominent technique for the acquisition of high-density and accurate topographic information. In a LiDAR system, the GNSS/INS-derived position and orientation information is integrated with the laser scanner measurements (ranges and mirror angles) to derive the ground coordinates of the laser-beam footprints. In general, the quality of the LiDAR derived coordinates can be evaluated using checkpoint analysis involving specifically-designed LiDAR targets. However, the utilization of LiDAR targets is quite expensive and is not always possible in all mapping missions. Moreover, LiDAR targets might not be appropriate to validate the presence of GNSS/INS position and orientation drifts in the collected data. This paper is dedicated to providing an accurate, economical, and convenient internal quality control procedure for the evaluation of LiDAR data, which is captured from parallel flight lines. The underlying concept of the proposed methodology is that in the absence of systematic and random errors in the system parameters and measurements, conjugate surface elements in overlapping strips should perfectly match with each other. The paper will prove that systematic errors in the system parameters will lead to consistent biases between conjugate surface elements in overlapping strips. Such a finding will be used to confirm the presence or absence of GNSS/INS drift errors in the derived position and orientation information. More specifically, the presence of inconsistent systematic biases between conjugate surface elements in overlapping strips will be used to detect drifts in the derived GNSS/INS position and orientation information. In addition, the average normal distances between conjugate surface elements, after removing existing discrepancies, will be used as an estimate of the magnitude of the random noise in the derived point cloud coordinates.
Published in:	Proceedings of the 2009 International Technical Meeting of The Institute of Navigation January 26 - 28, 2009 Disney's Paradise Pier Hotel Anaheim, CA
Pages:	753 - 766
Cite this article:	Habib, Ayman, Kersting, Ana Paula, Bang, Ki-In, "Detecting Systematic Biases and GNSS/INS Drifts in LiDAR Data," Proceedings of the 2009 International Technical Meeting of The Institute of Navigation, Anaheim, CA, January 2009, pp. 753-766.
Full Paper:	ION Members/Non-Members: 1 Download Credit Sign In