Automatic tie elements detection for laser scanner strip adjustment (original) (raw)
Related papers
Investigating adjustment of airborne laser scanning strips without usage of GNSS/IMU trajectory data
2009
Airborne laser scanning (ALS) requires GNSS (Global Navigation Satellite System; e.g. GPS) and an IMU (Inertial Measurement Unit) for determining the dynamically changing orientation of the scanning system. Because of small but existing instabilities of the involved parts-especially the mounting calibration-a strip adjustment is necessary in most cases. In order to realize this adjustment in a rigorous way the GNSS/IMU-trajectory data is required. In some projects this data is not available to the user (any more). Derived from the rigorous model, this article presents a model for strip adjustment without GNSS/IMU-trajectory data using five parameters per strip: one 3D shift, one roll angle, and one affine yaw parameter. In an example with real data consiting of 61 strips this model was successfully applied leading to an obvious improvement of the relative accuracy from (59.3/23.4/4.5) [cm] to (7.1/7.2/2.2) (defined as RMS values in (X/Y/Z) of the differences of corresponding points derived by least squares matching in the overlapping strips). This example also clearly demonstrates the importance of the affine yaw parameter.
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).
Smart Identification of Overlapping Strip Pairs/Regions for Optimized LiDAR System Calibration
International Conference on Aerospace Sciences & Aviation Technology, 2013
Recently, laser scanning systems, onboard airborne and terrestrial mobile mapping systems, have been established as a leading technology for collecting high density 3D information from an object's surface. The availability of generated surface models is very important for various industrial, military, environmental, and public applications. The accuracy of the derived point cloud coordinates from a LiDAR system is affected by inherent systematic and random errors. The impact of random errors depends on the precision of the system's measurements, which comprise position and orientation information from the GPS/INS unit, mirror angles, and ranges. On the other hand, systematic errors are mainly caused by biases in the mounting parameters (i.e., lever arm offset and boresight angles) relating the system components as well as biases in the system measurements (e.g., ranges and mirror angles). In order to ensure the geometric quality of the collected point cloud, the LiDAR systems should undergo a rigorous calibration procedure to estimate the system parameters that minimize the discrepancies between conjugate surface elements in overlapping LiDAR strips. The main objective of this paper is to look into an existing LiDAR system calibration technique, which is based on manual selection of overlapping regions between LiDAR strips and how to increase the efficiency of this technique by automatic selection of appropriate overlapping strip pairs, which should achieve the minimum optimal flight configuration that maximizes the impact of the discrepancies among conjugate surface elements in overlapping strips as well as automatic selection of regions within the appropriate overlapping strip pairs. The methodology of the proposed technique can be summarized as follows: first, the LiDAR strip pairs are grouped based on the flight configuration; second, appropriate overlapping strip pairs from each group is automatically selected; third, regions within the appropriate overlapping strip pairs are automatically selected based on their angles (slopes and aspects) and distribution; finally, the calibration procedure is applied. The experimental results have shown that the quality of the estimated parameters using the automatic selection are quite comparable to the estimated parameters using the manual selection while the proposed method is fully automated, and much faster.
Automatic Selection of Overlapping Strip Pairs/Regions for Optimized Lidar System Calibration
The International Conference on Electrical Engineering, 2016
Recently, laser scanning systems (airborne and terrestrial mobile mapping systems) have been established as a leading technology for collecting high density 3D information from an object's surface. The availability of generated surface models is very important for various industrial, military, environmental, and public applications. The accuracy of the derived point cloud coordinates from a LiDAR system is affected by inherent systematic and random errors. The impact of random errors depends on the precision of the system's measurements, which comprise position and orientation information from the GPS/INS unit, mirror angles, and ranges. On the other hand, systematic errors are mainly caused by biases in the mounting parameters (i.e., lever arm offset and boresight angles) relating the system components as well as biases in the system measurements (e.g., ranges and mirror angles). In order to ensure the geometric quality of the collected point cloud, the LiDAR systems should undergo a rigorous calibration procedure to estimate the system parameters that minimize the discrepancies between conjugate surface elements in overlapping LiDAR strips. The main objective of this paper is to look into an existing LiDAR system calibration technique, which is based on manual selection of overlapping regions between LiDAR strips and how to increase the efficiency of this technique by automatic selection of appropriate overlapping strip pairs, which should achieve the minimum optimal flight configuration that maximizes the impact of the discrepancies among conjugate surface elements in overlapping strips as well as automatic selection of regions within the appropriate overlapping strip pairs. The methodology of the proposed technique can be summarized as follows: first, the LiDAR strip pairs are grouped based on the flight configuration; second, appropriate overlapping strip pairs from each group is automatically selected; third, regions within the appropriate overlapping strip pairs are automatically selected based on their angles (slopes and aspects) and distribution; finally, the calibration procedure is applied. The experimental results have shown that the quality of the estimated parameters using the automatic selection are quite comparable to the estimated parameters using the manual selection while the proposed method is fully automated, and much faster.
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.
Estimating intrinsic accuracy of airborne laser data with local 3D-offsets
2003
Airborne laser systems provide a three-dimensional (3D) perception of the Earth's topography with clouds of points. Whereas the technique ensures a high theoritical quality, one can observe discrepancies in certain areas. This situation may be of importance in case of joint sensor application, like merging airborne laser scanner with photogrammetry. The first step of a fusion process is to define a common reference frame so that a global geometric coherence should be extracted. This article describes a methodology for matching a single laser strip with a photogrammetric derived Digital Elevation Model (DEM), and as a result estimating intra-strip errors. It is based on calculating local linear deformations with a tri-dimensionnal accumulator (translation space). We show that searching for local discrepancy is equivalent to compute the maximum of the accumulator. 2D and 3D simulated problems are discussed in details and solved over known transformed data set. Results on real data show a significant improvement when applying retrieved local translations to laser points. After correction, both data sets tend to be expressed in the same reference frame. The accurate registration is then ensured.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012
Integration of laser scanning data and photographs is an excellent combination regarding both redundancy and complementary. Applications of integration vary from sensor and data calibration to advanced classification and scene understanding. In this research, only airborne laser scanning and aerial images are considered. Currently, the initial registration is solved using direct orientation sensors GPS and inertial measurements. However, the accuracy is not usually sufficient for reliable integration of data sets, and thus the initial registration needs to be improved. A registration of data from different sources requires searching and measuring of accurate tie features. Usually, points, lines or planes are preferred as tie features. Therefore, the majority of resent methods rely highly on artificial objects, such as buildings, targets or road paintings. However, in many areas no such objects are available. For example in forestry areas, it would be advantageous to be able to improve registration between laser data and images without making additional ground measurements. Therefore, there is a need to solve registration using only natural features, such as vegetation and ground surfaces. Using vegetation as tie features is challenging, because the shape and even location of vegetation can change because of wind, for example. The aim of this article was to compare registration accuracies derived by using either artificial or natural tie features. The test area included urban objects as well as trees and other vegetation. In this area, two registrations were performed, firstly, using mainly built objects and, secondly, using only vegetation and ground surface. The registrations were solved applying the interactive orientation method. As a result, using artificial tie features leaded to a successful registration in all directions of the coordinate system axes. In the case of using natural tie features, however, the detection of correct heights was difficult causing also some tilt errors. The planimetric registration was accurate.
Planimetric offset adjustment of multitemporal laser scanner data
2008
LiDAR (Light Detection and Ranging) data have shown a great potential for 3D modelling applications. This potential lies on the ability of LiDAR systems to generate highly dense 3D point clouds for describing the terrain surface. Several error sources affect the position accuracy of the 3D points, which are represented as offsets between the overlapping areas. Several methods have been developed to correct these displacements using height or intensity data. This paper proposes a three steps procedure to correct the offset observed between a multitemporal dataset. Firstly intensity images were generated. Secondly, an area based image correlation technique was applied to extract evenly distributed control points. Finally the control points were used to determine the parameters of a global transformation by least squares. The technique showed good performance for the study area reducing significantly the planimetric discrepancies observed.
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.
ACCURACY STUDY OF AIRBORNE LASER SCANNING DATA WITH PHOTOGRAMMETRY
2001
This paper describes an accuracy study of airborne laser scanning data obtained by the Airborne Topographic Mapper (ATM) laser system over Ocean City, Md. The ATM is a conical scanning laser altimeter developed by NASA for precise measurement of surface elevation changes in polar ice sheets, ocean beaches and drainage systems. First, we determine the "internal" accuracy of the system by comparing data from different flight missions. This is followed by a comparison of the merged laser data sets with surface elevations obtained by photogrammetry. Large-scale aerial photographs have been acquired over the test area and an aerial triangulation was performed to determine the exterior orientation parameters. The comparison consists of several experiments that were performed with the digitized photographs and the laser points. First we determine how well the laser points agree with the visible surface as defined by two overlapping images (stereopsis). This is accomplished by backprojecting the laser points to the images based on their exterior orientation parameters. The location of the laser points in the images serve as initial approximations for image matching. We use an adaptive least-squares matching procedure with a variable template size. A non-zero matching vector indicates discrepancies between laser points and photogrammetry. The purpose of the second experiment is to estimate the horizontal accuracy of laser points. One way to accomplish this is to extract linear features and to compare them. Linear features in laser point data sets can only be determined indirectly, e.g. by intersecting planar surface patches. In contrast, linear features in aerial images can be determined directly by an edge operator. We used the Canny operator to extract edges in the images and feature-based matching to find corresponding edges in the stereopair. After describing the procedure, experimental results are reported.