Ki-In Bang

Alternative Methodologies for LiDAR System Calibration

Abstract: Over the last few years, LiDAR has become a popular technology for the direct acquisition of topographic information. In spite of the increasing utilization of this technology in several applications, its accuracy potential has not been fully explored. Most of current LiDAR calibration techniques are based on empirical and proprietary procedures that demand the system’s raw measurements, which may not be always available to the end-user. As a result, we can still observe systematic discrepancies between conjugate surface elements in overlapping LiDAR strips. In this paper, two alternative calibration procedures that overcome the existing limitations are introduced. The first procedure, denoted as ―Simplified method‖, makes use of the LiDAR point cloud from parallel LiDAR strips acquired by a steady platform (e.g., fixed wing aircraft) over an area with moderately varying elevation. The second procedure, denoted as ―Quasi-rigorous method‖, can deal with non-parallel strips, but requires time-tagged LiDAR point cloud and navigation data (trajectory position only) acquired by a steady platform. With the widespread adoption of LAS format and easy access to trajectory information, this data requirement is not a problem. The proposed methods can be applied in any type of terrain coverage without the need for control surfaces and are relatively easy to implement. Therefore, they can be used in every flight mission if needed. Besides, the proposed procedures require minimal interaction from the user, which can be completely eliminated after minor extension of the suggested procedure.

Comprehensive Analysis of Sensor Modeling Alternatives for High Resolution Imaging Satellites

High-resolution imaging satellites are a valuable and cost effective data acquisition tool for a variety of mapping and GIS applications such as topographic mapping, map updating, orthophoto generation, environmental monitoring, and change detection. Sensor modeling that describes the mathematical relationship between corresponding scene and object coordinates is a prerequisite procedure prior to manipulating the acquired imagery from such systems for mapping purposes. Rigorous and approximate sensor models are the two alternatives for describing the mathematics of the involved imaging process. The former explicitly involves the internal and external characteristics of the imaging sensor to faithfully represent the geometry of the scene formation. On the other hand, approximate modeling can be divided into two categories. The first category simplifies the rigorous model after making some assumptions about the system’s trajectory and/or object space. Gupta and Hartley’s model, parallel projection, self-calibrating direct linear transformation, and modified parallel projection are examples of this category. Other approximate models are based on empirical formulation of the scene-to-ground mathematical relationship. This category includes among others, the well-known Rational Function Model (RFM). This paper addresses several aspects of sensor modeling. Namely, it deals with the expected accuracy from rigorous modeling of imaging satellites as it relates to the number of available ground control points, comparative analysis of approximate and rigorous sensor models, robustness of the reconstruction process against biases in the available sensor characteristics, and impact of incorporating multi-source imagery in a single triangulation mechanism. Following a brief theoretical background, these issues will be presented through experimental results from real datasets captured by satellite and aerial imaging platforms.

Alternative Methodologies for the Internal Quality Control of Parallel LiDAR Strips

Light Detection and Ranging (LiDAR) systems have been widely adopted for the acquisition of dense and accurate topographic data over extended areas. Although the utilization of this technology has increased in different applications, the development of standard methodologies for the quality control (QC) of LiDAR data has not followed the same trend. In other words, a lack in reliable, practical, cost-effective, and commonly acceptable QC procedures is evident. A frequently adopted procedure for QC is comparing the LiDAR data to ground control points. Aside from being expensive, this approach is not accurate enough for the verification of horizontal accuracy, unless specifically designed LiDAR targets are used. This paper is dedicated to providing accurate, economical, and convenient internal QC procedures for the evaluation of LiDAR data, which is captured from parallel flight lines. The underlying concept of the proposed methodologies is that, in the absence of systematic and random errors in system parameters and measurements, conjugate surface elements in overlapping strips should perfectly match each other. Consistent incompatibilities and the quality of fit between conjugate surface elements in overlapping strips can be used to detect systematic errors in the system parameters/measurements and to evaluate the noise level in the LiDAR point cloud, respectively. Experimental results from real data demonstrate that all the proposed methods, with one exception, produce compatible estimates of systematic discrepancies between the involved data sets, as well as good quantification of inherent noise.

Automated approach for rigorous light detection and ranging system calibration without preprocessing and strict terrain coverage requirements

Light detection and ranging (LiDAR) has demonstrated its capabilities as a prominent technique for the direct acquisition of high density and accurate topographic information. To achieve the potential accuracy of such systems, a rigorous system calibration should be performed. We introduce a novel rigorous LiDAR system calibration procedure where the system parameters are determined by minimizing the discrepancies among conjugate surface elements in overlapping strips and control data, if available. The method is automated and does not require specific features (e.g., planes or lines) in the covered area or preclassification of the point cloud. Suitable primitives, which do not involve preprocessing of the data, are implemented. The correspondence between conjugate primitives is determined using a robust matching procedure. A modification to the Gauss Markov model is introduced to keep the implementation of the calibration procedure simple while utilizing higher order primitives. Experimental results have demonstrated the effectiveness of the proposed method over different types of terrain coverage

True Ortho-photo Generation from High Resolution Satellite Imagery

Ortho-photos contain highly valuable data with high potential. They are useful in various applications, such as the creation of image maps and texture information in Geographic Information System (GIS). The Z-buffer method has been one of the most popular methods for the true ortho-photo generation. However, it has strict requirements regarding the Digital Surface Model (DSM) cell size. Furthermore, Z-buffer Method have false visibility problmes in narrow vertical structrues. The Z-buffer Method can be modified to avoid this problem using pseudo points along vertical surfaces. However, this method still has problems with certain DSM resolutions. This study implements two technical approaches for true ortho-photo generation u(l) the horizontal position of a point along the search path reaches the nadir point in the object space sing high resolution satellite imagery. The first approach deals with the scan line search method, which is required since each scan line has its own perspective center. The other approach is concerned with occlusion detection, which is required for the creation of true ortho-photos. Final experimental results with real data have demonstrated the feasibility of this proposed true ortho-photo generation methodology.