Angular resolution of terrestrial laser scanners

Knowledge of a laser scanner's spatial resolution is necessary in order to prevent aliasing and estimate the level of detail that can be resolved from a scanned point cloud. Spatial resolution can be decoupled into range and angular components. The latter is the focus of this paper and is governed primarily by sampling interval and laser beamwidth. However, emphasis is often placed on one of these—typically sampling interval—as an indicator of resolution. Since both affect the resolution of a scanned point cloud, consideration of only one factor independent of the other can lead to a misunderstanding of a system's capabilities. This will be demonstrated to be inappropriate except under very specific conditions. A new, more appropriate resolution measure for laser scanners, the effective instantaneous field of view (EIFOV), is proposed. It is derived by modelling the shift variance of the equal angular increment sampling process, laser beamwidth-induced positional uncertainty and observed angle quantisation with ensemble average modulation transfer functions (AMTFs). Several commercially available terrestrial laser scanner systems are modelled and analysed in terms of their angular resolution capabilities using the EIFOV.     

Sensor modeling,self-calibration and accuracy testing of panoramic cameras and laser scanners

Terrestrial Linear Array CCD-based panoramic cameras have been used for purely imaging purposes, but they also have a high potential for use in high accuracy measurement applications. The imaging geometry and the high information content of those images make them suitable candidates for quantitative image analysis. For that a particular sensor model has to be established and the inherent accuracy potential has to be investigated. We developed a sensor model for terrestrial Linear Array-based panoramic cameras by means of a modified bundle adjustment with additional parameters, which models substantial deviations of a real camera from the ideal one. We used 3D straight-line information in addition to tie points to conduct a full calibration and orientation without control point information. Due to the similarity of the operation of laser scanners to panoramic cameras the sensor model of the panoramic cameras was extended for the self-calibration of laser scanners. We present the joint sensor model for panoramic cameras and laser scanners and the results of self-calibration, which indicate a subpixel accuracy level for such highly dynamic systems. Finally we demonstrate the systems’ accuracy of two typical panoramic cameras in 3D point positioning, using both a minimal number of control points and a free network adjustment. With these new panoramic imaging devices we have additional powerful sensors for image recording and efficient 3D object modeling.     

Parameter de-correlation and model-identification in hybrid-style terrestrial laser scanner self-calibration

One of the important systematic error parameters identified in terrestrial laser scanners is the collimation axis error, which models the non-orthogonality between two instrumental axes. The quality of this parameter determined by self-calibration, as measured by its estimated precision and its correlation with the tertiary rotation angle κ of the scanner exterior orientation, is strongly dependent on instrument architecture. While the quality is generally very high for panoramic-type scanners, it is comparably poor for hybrid-style instruments. Two methods for improving the quality of the collimation axis error in hybrid instrument self-calibration are proposed herein: (1) the inclusion of independent observations of the tertiary rotation angle κ; and (2) the use of a new collimation axis error model. Five real datasets were captured with two different hybrid-style scanners to test each method’s efficacy. While the first method achieves the desired outcome of complete decoupling of the collimation axis error from κ, it is shown that the high correlation is simply transferred to other model variables. The second method achieves partial parameter de-correlation to acceptable levels. Importantly, it does so without any adverse, secondary correlations and is therefore the method recommended for future use. Finally, systematic error model identification has been greatly aided in previous studies by graphical analyses of self-calibration residuals. This paper presents results showing the architecture dependence of this technique, revealing its limitations for hybrid scanners.     

An automatic procedure for co-registration of terrestrial laser scanners and digital cameras

Driven by progress in sensor technology, algorithms and data processing capabilities, close range photogrammetry has found a wide range of new application fields over the past two decades. Particularly, the emergence of terrestrial laser scanner has contributed to the close range photogrammetry “popularization” through many promising new applications. Nevertheless, a central issue in many of these developments is the integration of sensor technology with reliable data processing schemes to generate highly automated photogrammetric measurements systems.This paper presents a flexible approach for the automatic co-registration of terrestrial laser scanners (TLS) and amateur digital cameras (DC) to be used effectively in practice. Particularly, the developed approach deals with two different images: a camera image acquired with a DC and a range image obtained with a TLS. To this end, an open-source tool “USAlign” has been developed for testing the different experiments.     

Multi-feature based boresight self-calibration of a terrestrial mobile mapping system

This paper presents a multi-feature based system calibration method for estimating the boresight angles of a land-based mobile mapping system (MMS) comprised of multiple two dimensional (2D) scanners. The method invokes a least-squares adjustment (LSA) to simultaneously estimate several sets of boresight angles for multiple laser scanners incorporated in an MMS as well as the parameters associated with one or more types of geometric features. This is achieved by constraining the groups of feature point clouds captured by multiple runs to fit their corresponding geometric models in such a way that the weighted sum of squares of adjustment residuals is minimized. The method is particularly suitable for in situ calibration because the geometric features involved are commonly occurring structures (e.g. building façades, bridge surfaces, highway signs and hanging power cables) that are usually captured during the actual survey. In addition to using a planar feature model for calibration, a novel and rigorous three-dimensional (3D) catenary curve model is proposed for geometric modelling of hanging cables to augment the calibration. The proposed calibrations were examined with several different combinations of groups of planar and catenary features and the resulting analysis suggests that the in situ calibrations are effective when compared to the manufacturer’s dedicated calibration, with the overall point cloud accuracies for plane fitting being 5.5 cm and 5.4 cm for the vertical and horizontal directions, respectively. It has been successfully demonstrated that the proposed method can be used in a scene having no building façades but only some long hanging cables and horizontal ground surfaces. This is particularly useful for rural areas or inter-city/provincial highways where building façades cannot commonly be captured. Parameter correlations in the calibrations were also addressed. It has also been shown that using catenary features in addition to planar features for the calibration can help de-correlate some parameters and improve the overall accuracy. The in situ nature and the high flexibility of integrating different features of the calibration make the proposed method straightforward for most end-users.     

A unified approach to post-mission processing of intertial data

The outpout of inertial survey systems is available to the user in two basic forms: as Kalman filtered information at updates or as integrated velocity and position information at regular time intervals. In case of the second data type, the post-mission processing starts with the approximation of the velocity error curve. This approximation is either based on a system error model as in Kalman filtering or uses curve fitting techniques. From there on, smoothing or adjustment procedures are used as further steps in the post-mission treatment of both data types. A unified treatment of the various post-mission approaches starts with the formulation of appropriate error models of the system outputs. It is then possible to present all existing methods as intermediate steps of a rigorous adjustment procedure. This unified approach gives insight into the limitations of individual methods and provides a means to detect inconsistencies in post-mission processing strategies. An analysis of existing approaches is made and a new method, spectral decomposition, is treated in detail. Compared to the existing procedures, it has advantages with respect to a rigorous covariance propagation and blunder detection.     

A unified approach to post-mission processing of intertial data

The outpout of inertial survey systems is available to the user in two basic forms: as Kalman filtered information at updates or as integrated velocity and position information at regular time intervals. In case of the second data type, the post-mission processing starts with the approximation of the velocity error curve. This approximation is either based on a system error model as in Kalman filtering or uses curve fitting techniques. From there on, smoothing or adjustment procedures are used as further steps in the post-mission treatment of both data types. A unified treatment of the various post-mission approaches starts with the formulation of appropriate error models of the system outputs. It is then possible to present all existing methods as intermediate steps of a rigorous adjustment procedure. This unified approach gives insight into the limitations of individual methods and provides a means to detect inconsistencies in post-mission processing strategies. An analysis of existing approaches is made and a new method, spectral decomposition, is treated in detail. Compared to the existing procedures, it has advantages with respect to a rigorous covariance propagation and blunder detection. Paper presented at the 18^th. General Assembly of the IAG, Symposium d: The Future of Terrestrial and Space Methods for Positioning, Hamburg, August 15–27, 1983.     

An integrated bundle adjustment approach to range camera geometric self-calibration

This paper describes a new method for integrated range camera system self-calibration in which both traditional camera calibration parameters and rangefinder systematic error parameters are estimated simultaneously in a free-network bundle adjustment of observations to signalised targets. Its mathematical basis is collinearity and range observation equations augmented with correction models for systematic error sources identified in the data. The self-calibration results from datasets captured with two different range cameras, a SwissRanger SR3000 and a SwissRanger SR4000, are presented and analysed in detail. The method’s effectiveness is demonstrated in terms of systematic error removal and independent accuracy assessment. Up to a 54% reduction in the residual RMS was achieved by inclusion of the proposed error models in the self-calibration adjustment. An improvement of at least 74% in the RMS of object point co-ordinate differences, over that achieved without calibration or provided by the manufacturer’s software (in the case of the SR3000), was realised in an independent accuracy assessment. In addition, the effects of several influencing variables, including the range stochastic error model, the network geometry and the range measurements themselves, on key correlation mechanisms are analysed in detail.     

地面三维激光扫描技术在公路建模中的应用

为解决传统技术获取公路数据效率低,数据不够精确与详细的问题,本文提出了将地面三维激光扫描技术应用于公路建模的数据采集方案和数据处理方法。根据地面激光扫描的特点和公路建模要求,设计出按最佳扫描距离进行分段设站、分块扫描的数据采集方案。经过点云配准和滤波等预处理后,利用点云几何信息进行了数字表面模型、等高线、纵横断面等模型生成。本文以Trimble GS200地面三维激光扫描仪为例对高速公路路面进行数据采集,以Realworks Survey5.0作为建模工具进行路面建模。结果表明采集方案能提高获取原始数据的效率,并且保证了数据质量,降低了数据处理的复杂程度。     

珠海市陆海统一似大地水准面的确定

针对通常的似大地水准面模型较少涉及海域的情况,该文基于重力数据和地形数据,采用顾及各类地形位及地形引力影响的第二类Helmert凝集法计算了珠海重力似大地水准面;利用高分辨率和高精度的地形数据来恢复大地水准面短波部分,提高了似大地水准面的精度;利用25个高精度全球卫星导航系统水准资料与重力似大地水准面进行了独立比较,其精度为0.012m;然后,采用球冠谐方法,将重力似大地水准面与25个全球卫星导航系统水准数据联合,建立了珠海市海陆统一的似大地水准面模型,其精度为0.008m;最后,利用15个全球卫星导航系统/水准点对似大地水准面模型进行了外部检核,精度为0.010m。     

Terrestrial laser scanner self-calibration: Correlation sources and their mitigation

Instrument calibration is recognised as an important process to assure the quality of data captured with a terrestrial laser scanner. While the self-calibration approach can provide optimal estimates of systematic error parameters without the need for specialised equipment or facilities, its success is somewhat hindered by high correlations between model variables. This paper presents the findings of a detailed study into the sources of correlation in terrestrial laser scanner self-calibration for a basic additional parameter set. Several pertinent outcomes, resulting from experiments conducted with simulated data, and 12 real calibration datasets captured with a Faro 880 terrestrial laser scanner, are presented. First, it is demonstrated that panoramic-type scanner self-calibration from only two instrument locations is possible so long as the scans have orthogonal orientation in the horizontal plane. Second, the importance of including scanner tilt angle observations in the adjustment for parameter de-correlation is demonstrated. Third, a new network measure featuring an asymmetric distribution of object points that does not rely upon a priori observation of the instrument position is proposed. It is shown to be an effective means to reduce the correlation between the rangefinder offset and the scanner position parameters. Fourth, the roles of several other influencing variables on parameter correlation are revealed. The paper concludes with a set of recommended design measures to reduce parameter correlation in terrestrial laser scanner self-calibration.     

A new integral approach to geopotential coefficient determinations from terrestrial gravity or satellite altimetry data

The spherical harmonic coefficients of the Earth’s gravitational potential are conveniently determined by integration of gravity data or potential data (derived from satellite altimetry) over a sphere. The major problem of such a method is that the data, given on the non-spherical surface of the Earth, must be reduced to the sphere. A new integral formula over the surface of the Earth is derived. With this formula improved first order topographic corrections to the spherical formulas are obtained.     

地面激光扫描中激光强度值的影响因素及改正方法

激光强度作为地面三维激光扫描仪特有的测量值,在现有三维激光扫描仪中并没有得到有效利用.鉴于此,文章从理论上推导了激光强度值的影响因素,根据强度值的影响因素提出了一种强度值的改正方法,并通过实验对理论模型进行了验证分析.实验结果表明:本文提出的改正方法能较好地对强度值进行改正,对相关的硬件制造和应用具有一定的价值.     

Conventional terrestrial system by VLBI a kinematic approach

The paper examines the potential ofVLBI time delay observables for the establishment and maintenance of a Conventional Terrestrial System (CTS). TheCTS is defined in2-D by the standard epoch positions and velocities of a network of control points located on a spherical reference surface. VLBI time delay observables are sensitive to the rotational motion of theCTS control points with respect to a Conventional Inertial System (CIS) which is represented by a network of radio sources. The motion of a control point with respect to theCIS is partitioned into global and regional components. The global components represent the rotational motion of the sphere with respect to theCIS, while the regional components represent the motion of theCTS points with respect to the sphere.     

利用地面激光扫描数据提取单木结构参数

针对现有提取单木结构参数的方法精度不高的现状,文章研究利用地面三维激光扫描数据提取单木的高度、胸径和冠幅,特别是将RANSAC算法用于圆拟合,提高了胸径的反演精度。最后利用野外实测数据进行验证,并比较分析了Hough变换和RANSAC算法在提取胸径中的差异。结果表明,本文方法提取的单木参数精度较高;并且无论从相关系数、平均残差还是均方根误差等方面,RANSAC算法提取的胸径精度均高于Hough变换方法。     

Ground filtering and vegetation mapping using multi-return terrestrial laser scanning

Discriminating laser scanner data points belonging to ground from points above-ground (vegetation or buildings) is a key issue in research. Methods for filtering points into ground and non-ground classes have been widely studied mostly on datasets derived from airborne laser scanners, less so for terrestrial laser scanners. Recent developments in terrestrial laser sensors (longer ranges, faster acquisition and multiple return echoes) has aroused greater interest for surface modelling applications. The downside of TLS is that a typical dataset has high variability in point density, with evident side-effects on processing methods and CPU-time. In this work we use a scan dataset from a sensor which returns multiple target echoes, in this case providing more than 70 million points on our study site. The area presents low, medium and high vegetation, undergrowth with varying density, as well as bare ground with varying morphology (i.e. very steep slopes as well as flat areas). We test an integrated work-flow for defining a terrain and surface model (DTM and DSM) and successively for extracting information on vegetation density and height distribution on such a complex environment. Attention was given to efficiency and speed of processing. The method consists on a first step which subsets the original points to define ground candidates by taking into account the ordinal return number and the amplitude. A custom progressive morphological filter (opening operation) is applied next, on ground candidate points using a multidimensional grid to account for the fallout in point density as a function of distance from scanner. Vegetation density mapping over the area is then estimated using a weighted ratio of point counts in the tri-dimensional space over each cell. The overall result is a pipeline for processing TLS points clouds with minimal user interaction, producing a Digital Terrain Model (DTM), a Digital Surface Model (DSM), a vegetation density map and a derived Canopy Height Model (CHM). These products are of high importance for many applications ranging from forestry to hydrology and geomorphology.