Investigating adjustment of airborne laser scanning strips without usage of GNSS/IMU trajectory data (original) (raw)
Related papers
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2011
In this article we extend our previous work on the topic of ALS strip adjustment without GNSS/IMU trajectory data. Between overlapping strip pairs the relative orientation as a 3D affine transformation is estimated by a 3D LSM approach, which uses interpolated 2.5D grid surface models of the strips and the entire strip overlap as one big LSM window. The LSM derived relative orientations of all strip pairs in the block together with their covariance matrices are then used simultaneously as observations in an adjustment following the Gauss-Helmert model. This way the exterior orientation of each strip is computed, which refers to a relative block system. If proper ground control data is given, then an absolute orientation of the block of strips can be computed by a final LSM run. In a small example consisting of 4 strips with ca. 70% overlap the improvement in the relative geometric accuracy is demonstrated by the decreasing σ MAD of the height differences from 8.4cm (before) to 1.6cm (after the strip adjustment).
Automatic tie elements detection for laser scanner strip adjustment
2005
Measurements with airborne laser scanners are performed in strips, usually with multiple length strips and a few cross strips. Due to i) wrong or inaccurate calibration of the entire measurement system and due to ii) the limited accuracy of the exterior orientation determination with GPS and IMU and systematic errors of these devices, adjacent strips can show discrepancies in their overlap. For removing these discrepancies strip adjustment algorithms require quantification on these offsets at various locations within the overlapping zones. We present a general method for determining these discrepancies automatically based on segmentation of the overlap. A method to determine the accuracy of these discrepancy observations is demonstrated as well. In the examples we reconstruct mean offsets between neighbouring strips of a few centimetres, which, also show substantial variation along the strip axis. The accuracy of this discrepancy observations is in the order of 2cm. The method developed for discrepancy determination can be applied to height or full 3D strip adjustment. It can be used for approaches using the original measurements, the coordinates of the measured points, or only the offsets between surfaces.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2022, 161–168, 2022
In airborne laser scanning a high-frequency trajectory solution is typically determined from inertial sensors and employed to directly geo-reference the acquired laser points. When low-cost MEMS inertial sensors are used, such as in lightweight unmanned aerial vehicles, non-negligible errors in the estimated trajectory project to the final point-cloud, resulting in unsatisfactory accuracy on the ground. There are different multi-sensor fusion approaches to correct the point-cloud errors caused by an imperfect trajectory determination. Mismatches between different optical observations and/or in the overlapping regions of the point-cloud can allow the correction of the final point-cloud, either directly, by means of rigid transformations, or indirectly, via improving the scanner trajectory estimation. In this work we propose to fuse lidar and cameras in a single adjustment based on dynamic networks, considering 2D tie-points from the imagery and 3D tie-points from overlapping point-cloud sections. On a challenging corridor mapping scenario, we show that considering either 2D or 3D tie-points, along with inertial and GNSS observations, results in a remarkably accurate point-cloud, even when low-cost inertial sensors are employed and in presence of challenging surface textures, such as high vegetation. Furthermore, since the distribution of the 2D and 3D tie-points is complementary, considering them together further increases the robustness of the adjustment due to higher redundancy. By employing the proposed approach within this controlled example, we were able to improve the final point-cloud accuracy by more than three times with respect to conventional geo-referencing methodology and to reduce the magnitude of the errors.
Foundations for Strip Adjustment of Airborne Laserscanning Data with Conformal Geometric Algebra
Advances in Applied Clifford Algebras, 2021
Typically, airborne laserscanning includes a laser mounted on an airplane or drone (its pulsed beam direction can scan in flight direction and perpendicular to it) an intertial positioning system of gyroscopes, and a global navigation satellite system. The data, relative orientation and relative distance of these three systems are combined in computing strips of ground surface point locations in an earth fixed coordinate system. Finally, all laserscanning strips are combined via iterative closes point methods to an interactive three-dimensional terrain map. In this work we describe the mathematical framework for how to use the iterative closest point method for the adjustment of the airborne laserscanning data strips in the framework of conformal geometric algebra.
Strip Adjustment of LIDAR data
LIght Detection And Ranging (LIDAR) is a technique, which allows for measuring a huge amount of object point coordinates with accuracies up to some centimeters in short time. Their conversion into a highly accurate Digital Terrain Model (DTM) requires a careful handling of the single processing steps, from the flight planning until the manual revision of the generated DTM, considering the different error sources. In addition it must be ensured, that the LIDAR system maintains correctly calibrated during all the flight sessions.
Kinematic GPS survey as validation of LIDAR strips accuracy
As a result of the catastrophic hydrogeological events which occurred in May 1998 in Campania, in the south of Italy, the distinctive features of airborne laser scanning mounted on a helicopter were used to survey the landslides at Sarno and Quindici. In order to survey the entire zone of interest, approximately 21 km2, it was necessary to scan 12 laser strips. Many problems arose during the survey: difficulties in receiving the GPS signal, complex terrain features and unfavorable atmospheric conditions. These problems were investigated and it emerged that one of the most influential factors is the quality of GPS signals. By analysing the original GPS data, the traces obtained by fixing phase ambiguity with an On The Fly (OTF) algorithm were isolated from those with smoothed differential GPS solution (DGPS). Processing and analysis of laser data showed that not all the overlapping laser strips were congruent with each other. Since an external survey to verify the laser data accuracy...
Dual Airborne Laser Scanners Aided Inertial for Improved Autonomous Navigation
IEEE Transactions on Aerospace and Electronic Systems, 2009
A dead-reckoning terrain referenced navigation (TRN) system is presented that uses two airborne laser scanners (ALS) to aid an inertial navigation system (INS). The system uses aircraft autonomous sensors and is capable of performing the dual functions of mapping and navigation simultaneously. The proposed system can potentially serve as a backup to the Global Positioning System (GPS), increase the robustness of GPS or it can be used to coast for extended periods of time. Although the system has elements of a conventional TRN system, it does not require a terrain database since its in-flight mapping capability generates the terrain data for navigation. Hence, the system can be used in both non-GPS as well as unknown terrain environments. It is shown that the navigation system is dead-reckoning in nature since errors accumulate over time, unless the system can be reset periodically by the availability of geo-referenced terrain data or a position estimate from another navaid. Results of the algorithm using a combination of flight trajectory data and synthesized ALS data are presented.
Steps towards Quality Improvement of Airborne Laser Scanner Data
1998
Airborne laser scanning is a fast, cost-effective technique for the acquisition of 2.5D data, mainly for use in topographic and mapping operations. In recent years, however, the range of applications in which laser scanning can be used has greatly broadened and this has made higher data quality a necessity.
Applied sciences, 2019
A new statistical method for the quality control of the positional accuracy, useful in a wide range of data sets, is proposed and its use is illustrated through its application to airborne laser scanner (ALS) data. The quality control method is based on the use of a multinomial distribution that categorizes cases of errors according to metric tolerances. The use of the multinomial distribution is a very novel and powerful approach to the problem of evaluating positional accuracy, since it allows for eliminating the need for a parametric model for positional errors. Three different study cases based on ALS data (infrastructure, urban, and natural cases) that contain non-normal errors were used. Three positional accuracy controls with different tolerances were developed. In two of the control cases, the tolerances were defined by a Gaussian model, and in the third control case, the tolerances were defined from the quantiles of the observed error distribution. The analysis of the test results based on the type I and type II errors show that the method is able to control the positional accuracy of freely distributed data.
Rigorous approach to bore-sight self-calibration in airborne laser scanning
ISPRS Journal of Photogrammetry and Remote Sensing, 2006
We present a rigorous method for estimating some of the calibration parameters in airborne laser scanning (ALS), namely the three bore-sight angles and the range-finder offset. The technique is based on expressing the system calibration parameters within the directgeoreferencing equation separately for each target point, and conditioning a group of points to lie on a common surface of a known form such as a plane. However, the assumed a priori information about q chosen planar features is only their form not the spatial orientation or position. Thus, the 4·q plane parameters are estimated together with the calibration parameters in a combined adjustment model that makes use of GPS/INS-derived position and orientation as well as LiDAR range and encoder angle as observations. To make the approach practical when working with voluminous ALS and GPS/INS data, the contribution of each laser point to the normal equations is formed sequentially. The discussions focus on practical examples with data from a continuouslyrotating scanner that reveal the conditions under which almost complete de-correlation between the estimated parameters occurs. In such a case, all bore-sight angles are determined with accuracy that is several times superior to the system noise level. Given sufficiently strong geometry, the presented method is shown to be not only accurate but also very robust in terms of convergence. When appropriate, the method is applicable for calibration of additional systematic effects such as laser-beam encoder offsets or scale factor with minimal modification to the functional model.