Influence of surface reflectivity on reflectorless electronic distance measurement and terrestrial laser scanning

The uncertainty of electronic distance measurement to surfaces rather than to dedicated precisionre flectors (reflectorless EDM) is afected by the entire system comprising instrument, atmosphere and surface. The impact of the latter is significant for applications like geodetic monitoring, high-precision surface modelling or laser scanner self-calibration. Nevertheless, it has not yet received sufficient attention and is not well understood. We have carried out an experimental investigation of the impact of surface reflectivity on the distance measurements of a terrestrial laser scanner. The investigation helps to clarify (i)whether variations of reflectivity cause systematic deviations of reflectorless EDM, and (ii) if so, whether it is possible and worth modelling these deviations. The results show that differences in reflectivity may actually cause systematic deviations of a few mm with diffusely re- flecting surfaces and even more with directionally reflecting ones. Using abivariate quadratic polynomial we were able to approximate these deviations as a function of measured distance and measured signal strength alone. Using this approximation to predict corrections, the deviations of the measurements could be reduced by about 70% in our experiment.We conclude that there is a systematic effect of surface reflectivity (or equivalently received signal strength) on the distance measurement and that it is possible to model and predict this effect. Integration into laser scanner calibration models may be beneficial for high precision applications. The results may apply to a broad range of instruments, not only to the specific laser scanner used herei

Analysis of mobile laser scanning data and multi-view image reconstruction

The combination of laser scanning (LS, active, direct 3D measurement of the object surface) and photogrammetry (high geometric and radiometric resolution) is widely applied for object reconstruction (e.g. architecture, topography, monitoring, archaeology). Usually the results are a coloured point cloud or a textured mesh. The geometry is typically generated from the laser scanning point cloud and the radiometric information is the result of image acquisition. In the last years, next to significant developments in static (terrestrial LS) and kinematic LS (airborne and mobile LS) hardware and software, research in computer vision and photogrammetry lead to advanced automated procedures in image orientation and image matching. These methods allow a highly automated generation of 3D geometry just based on image data. Founded on advanced feature detector techniques (like SIFT (Scale Invariant Feature Transform)) very robust techniques for image orientation were established (cf. Bundler). In a subsequent step, dense multi-view stereo reconstruction algorithms allow the generation of very dense 3D point clouds that represent the scene geometry (cf. Patch-based Multi-View Stereo (PMVS2)). Within this paper the usage of mobile laser scanning (MLS) and simultaneously acquired image data for an advanced integrated scene reconstruction is studied. For the analysis the geometry of a scene is generated by both techniques independently. Then, the paper focuses on the quality assessment of both techniques. This includes a quality analysis of the individual surface models and a comparison of the direct georeferencing of the images using positional and orientation data of the on board GNSS-INS system and the indirect georeferencing of the imagery by automatic image orientation. For the practical evaluation a dataset from an archaeological monument is utilised. Based on the gained knowledge a discussion of the results is provided and a future strategy for the integration of both techniques is proposed

Geometry, constraints and computation of the trifocal tensor

Zsfassung in dt. Sprache20

2006: Overview and experiences in automated markerless image orientation

Automated image orientation is still a key problem in close-range photogrammetry, in particular if wide baseline images are employed. Nowadays, within the image-based modeling pipeline, the orientation step is the one which could be fully and reliably automated, exploiting the potentiality of computer and image processing algorithms. In this paper, we summarize recent developments in this field and apply them in three different workflows to automatically extract markerless tie points from closerange images of different types (video sequence, large and wide baseline images). Furthermore we compare the results obtained from bundle block adjustment using the automatic tie points with the results obtained by manual measurements and show how the accuracies of the automatic tie point extraction can further be improved by including least squares matching techniques. 1