On the Boresight Calibration and Point Cloud Matching of Airborne LiDAR
| Autor: | Jung-kuan Liu, 劉榮寬 |
|---|---|
| Rok vydání: | 2005 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 94 Airborne laser scanning (ALS), also known as “airborne LiDAR”, is an active remote sensing technique to capture surface terrain. The system is based on laser distance measurement, combined with a scanning mirror mechanism. As the potential of ALS becomes more promising, issues related to accuracy assessment, registration and data calibration receive increasing attention. Systematic errors in point clouds acquired by ALS may occur for many reasons. Three components of a laser system, namely, position (GPS), navigation (IMU), and range (laser scanner system), are sources of systematic errors. This dissertation presents a complete framework on handling the systematic errors in addressing system calibration, systematic error validation and remaining systematic error recovery. For system calibration, each boresight misalignment parameter is discussed to assess its impact on data accuracy and methodology of recovery. The schemes on boresight calibration solution used by two different commercial systems are introduced and the improvement on one of these approaches is proposed. The in-situ data set from a calibration flight is used to evaluate the improvement on the accuracy of misalignment parameters. A surface matching method, i.e. the ICP algorithm, is proposed, for the validation of the calibrated point clouds. In addition, the ICP algorithm provides the benefit of avoiding the need to interpolate the raw laser points, and evaluating the height as well as the planimetry offsets from overlapping laser strips. To evaluate the performance of the algorithm across different data quality level, two data sets are tested. The results reveal that the ICP algorithm can be used to both quantify the discrepancies from overlapping strips, and identify a solution regarding the correspondence problem. The remaining systematic errors can be affirmed by using the proposed surface matching technique. Next, this research presents a strip adjustment procedure for the recovery of data with remaining systematic errors. Two methods are applied. The first one is the three-dimensional (3-D) similarity transformation, i.e. the seven-parameter transformation between two 3-D data sets. The second one is the strip adjustment using three parameters to adjust the laser strips when not enough ground reference points are available. Meanwhile, the corresponding points derived from ICP matching are used to form the observations to implement the adjustment. The two proposed methods of strip adjustment confirm the following: (1) the corresponding points from ICP matching are sufficient to form the observations to implement adjustment; (2) the two methods can recover systematic error, especially on height. Analysis of the proposed solution on corresponding finding is then presented. Finally, a scheme on the accuracy assessment as well as remaining systematic errors recovery for ALS data is proposed. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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