Surface Matching Strategy for Quality Control of LIDAR Data

Abstract

Light Detection and Ranging (LIDAR) is emerging as a fast and accurate technology for acquiring 3D coordinates of object space points at high density. The accuracy of the collected data depends on the data acquisition procedure and the calibration quality of the involved sub-systems. Unlike photogrammetric cameras, current LIDAR systems do not furnish the capability of system calibration by the end users. In other words, LIDAR technology can be considered as a black box from the end user’s perspective. Therefore, the users are left with quality control procedures as the only means for ensuring data integrity, correctness, and completeness. Several methods are commonly used for LIDAR quality control, such as comparing interpolated range and intensity images or checking the coincidence of conjugate features extracted from overlapping LIDAR strips. However, these approaches require post-processing of the LIDAR data in the form of interpolation, segmentation, and/or feature extraction. Such procedures are time consuming and might introduce artefacts, which will influence the quality control outcome. Therefore, this research is focusing on developing a quality control approach based on surface matching of raw LIDAR data without any post-processing. The algorithm establishes the correspondence between overlapping LIDAR surfaces and estimates the transformation parameters (e.g., translations and rotations) relating them. In the absence of any biases, zero translations and rotations between overlapping strips should be expected. Thus, any deviations from these values indicate a bias in the data acquisition system. Experimental results from real data have shown the feasibility of using the proposed algorithm for detecting biases between overlapping LIDAR strips. This finding has been confirmed by comparing the results with these derived using extracted conjugate linear features from the same data.