Ana Paula Kersting

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.

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.

Error Budget of Lidar Systems and Quality Control of the Derived Data

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 assurance of lidar systems and quality control of the derived data has not followed the same trend. In other words, a lack of reliable, practical, cost-effective, and commonly-acceptable methods for quality evaluation is evident. A frequently adopted procedure for quality evaluation is the comparison between lidar data and ground control points. Besides being expensive, this approach is not accurate enough for the verification of the horizontal accuracy, which is known to be worse than the vertical accuracy. This paper is dedicated to providing an accurate, economical, and convenient quality control methodology for the evaluation of lidar data. The paper starts with a brief discussion of the lidar mathematical model, which is followed by an analysis of possible random and systematic errors and their impact on the resulting surface. Based on the discussion of error sources and their impact, a tool for evaluating the quality of the derived surface is proposed. In addition to the verification of the data quality, the proposed method can be used for evaluating the system parameters and measurements. Experimental results from simulated and real data demonstrate the feasibility of the proposed tool.

Direct Sensor Orientation of a Land-Based Mobile Mapping System

A land-based mobile mapping system (MMS) is flexible and useful for the acquisition of road environment geospatial information. It integrates a set of imaging sensors and a position and orientation system (POS). The positioning quality of such systems is highly dependent on the accuracy of the utilized POS. This limitation is the major drawback due to the elevated cost associated with high-end GPS/INS units, particularly the inertial system. The potential accuracy of the direct sensor orientation depends on the architecture and quality of the GPS/INS integration process as well as the validity of the system calibration (i.e., calibration of the individual sensors as well as the system mounting parameters). In this paper, a novel single-step procedure using integrated sensor orientation with relative orientation constraint for the estimation of the mounting parameters is introduced. A comparative analysis between the proposed single-step and the traditional two-step procedure is carried out. Moreover, the estimated mounting parameters using the different methods are used in a direct geo-referencing procedure to evaluate their performance and the feasibility of the implemented system. Experimental results show that the proposed system using single-step system calibration method can achieve high 3D positioning accuracy.

Geometric Calibration and Radiometric Correction of LiDAR Data and Their Impact on the Quality of Derived Products

LiDAR (Light Detection And Ranging) systems are capable of providing 3D positional and spectral information (in the utilized spectrum range) of the mapped surface. Due to systematic errors in the system parameters and measurements, LiDAR systems require geometric calibration and radiometric correction of the intensity data in order to maximize the benefit from the collected positional and spectral information. This paper presents a practical approach for the geometric calibration of LiDAR systems and radiometric correction of collected intensity data while investigating their impact on the quality of the derived products. The proposed approach includes the use of a quasi-rigorous geometric calibration and the radar equation for the radiometric correction of intensity data. The proposed quasi-rigorous calibration procedure requires time-tagged point cloud and trajectory position data, which are available to most of the data users. The paper presents a methodology for evaluating the impact of the geometric calibration on the relative and absolute accuracy of the LiDAR point cloud. Furthermore, the impact of the geometric calibration and radiometric correction on land cover classification accuracy is investigated. The feasibility of the proposed methods and their impact on the derived products are demonstrated through experimental results using real data.

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

Estimation of biases in lidar system calibration parameters using overlapping strips

Light detection and ranging (lidar) system calibration is essential to ensure the positional accuracy of the derived point cloud. Current lidar self-calibration techniques require full access to the system parameters and raw measurements (e.g., platform position and orientation, laser ranges, and scan mirror angles). Unfortunately, the raw measurements are not always available to the end-users. The absence of such information is limiting the widespread adoption of lidar calibration activities by the end-user. This paper proposes two alternative procedures for lidar system calibration, namely simplified and quasi-rigorous methods, which do not require the system raw measurements. The simplified method uses the lidar point cloud coordinates in overlapping parallel strips over terrain with moderate elevation variation to estimate biases in the system parameters. In this approach, the conventional lidar georeferencing equation is simplified based on a few reasonable assumptions. The quasi-rigorous method, on the other hand, is proposed to handle heading variations and varying terrain elevations using the time-tagged point coordinates and trajectory position data. Experimental results from simulated and real datasets showed that the proposed methods successfully estimated biases in system parameters, which produced more precise lidar points.