An Automatic Procedure For Mobile Laser Scanning Platform 6DOF Trajectory Adjustment

In this paper, a method is presented to improve the MLS platform’s trajectory for GNSS denied areas. The method comprises two major steps. The first step is based on a 2D image registration technique described in our previous publication. Internally, this registration technique first performs aerial to aerial image matching, this issues correspondences which enable to compute the 3D tie points by multiview triangulation. Similarly, it registers the rasterized Mobile Laser Scanning Point Cloud (MLSPC) patches with the multiple related aerial image patches. The later registration provides the correspondence between the aerial to aerial tie points and the MLSPC’s 3D points. In the second step, which is described in this paper, a procedure utilizes three kinds of observations to improve the MLS platform’s trajectory. The first type of observation is the set of 3D tie points computed automatically in the previous step (and are already available), the second type of observation is based on IMU readings and the third type of observation is soft-constraint over related pose parameters. In this situation, the 3D tie points are considered accurate and precise observations, since they provide both locally and globally strict constraints, whereas the IMU observations and soft-constraints only provide locally precise constraints. For 6DOF trajectory representation, first, the pose [R, t] parameters are converted to 6 B-spline functions over time. Then for the trajectory adjustment, the coefficients of B-splines are updated from the established observations. We tested our method on an MLS data set acquired at a test area in Rotterdam, and verified the trajectory improvement by evaluation with independently and manually measured GCPs. After the adjustment, the trajectory has achieved the accuracy of RMSE X = 9 cm, Y = 14 cm and Z = 14 cm. Analysing the error in the updated trajectory suggests that our procedure is effective at adjusting the 6DOF trajectory and to regenerate a reliable MLSPC product

Benchmarking of airborne laser scanning based feature extraction methods and mobile laser scanning system performance based on high-quality test fields

Osajulkaisut: Publication 1: Kaartinen, H., Hyyppä, J., Gülch, E., Vosselman, G., Hyyppä, H., Matikainen, L., Hofmann, A.D., Mäder, U., Persson, Å., Söderman, U., Elmqvist, M., Ruiz, A., Dragoja, M., Flamanc, D., Maillet, G., Kersten, T., Carl, J., Hau, R., Wild, E., Frederiksen, L., Holmgaard, J. and Vester, K., 2005. Accuracy of 3D city models: EuroSDR comparison. Proceedings of ISPRS Workshop "Laser scanning 2005", September 12-14, 2005, Enschede, The Netherlands, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVI(Part 3/W19), 227-232, CD-ROM. Publication 2: Kaartinen, H. and Hyyppä, J., 2006. EuroSDR-Project Commission 3 "Evaluation of Building Extraction", Final Report, In: EuroSDR - European Spatial Data Research, Official Publication No 50, 9-77. Publication 3: Kaartinen, H. and Hyyppä, J., 2008. EuroSDR/ISPRS Project, Commission II "Tree Extraction", Final Report, EuroSDR – European Spatial Data Research, Official Publication No 53, 56 p. Publication 4: Kaartinen, H., Hyyppä, J., Yu, X., Vastaranta, M., Hyyppä, H., Kukko, A., Holopainen, M., Heipke, C., Hirschmugl, M., Morsdorf, F., Næsset, E., Pitkänen, J., Popescu, S., Solberg, S., Wolf, B.M. and Wu, J.-C., 2012. An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning. Remote Sensing, 4(4), 950-974. Publication 5: Kaartinen, H., Hyyppä, J., Kukko, A., Jaakkola, A. and Hyyppä, H., 2012. Benchmarking the Performance of Mobile Laser Scanning Systems Using a Permanent Test Field. Sensors 12(9), 12814-12835. Publication 6: Kaartinen, H., Hyyppä, J., Kukko, A., Lehtomäki, M., Jaakkola, A., Hyyppä, H., Vosselman,G., Elberink, S.O., Rutzinger; M., Pu, S. and Vaaja, M., 2013. EuroSDR-Project Commission II "Mobile Mapping - Road Environment Mapping using Mobile Laser Scanning", Final Report, In: EuroSDR - European Spatial Data Research, Official Publication No 62, 49-95.Comparing different feature extraction methods based on remote sensing or remote sensing systems is difficult as there are but few common data sets or test fields with reference data of high standard available for analysis. State-of-the-art methods and systems are often in still evolving stage and can be run only by the developers themselves. Establishing a high-quality test field is laborious, but once such a test field has been established, it becomes easier to set up the systems to collect data from the field than to collect reference data from new areas. Comparing either different systems or the same system with different parameters is easier when the number of variables is kept to a minimum; the remotely sensed areas are kept constant and any changes in them can be controlled more easily. The benchmarking results provide valuable information to both developers and users of remote sensing data products. The benchmarked feature extraction methods studied included extraction of buildings and individual trees using data from common test fields. The performance of the mobile laser scanning systems was benchmarked using data collected from an established urban test field. In all cases, it was concluded that the primary factor affecting the results was the method or the system, and this enabled a high degree of comparability for the results of the given extraction or mapping tasks.Erilaisten kaukokartoitukseen perustuvien kohdemallinnusmenetelmien tai kaukokartoitusjärjestelmien vertailu on vaikeaa koska yhteisesti käytettävissä olevia aineistoja tai testikenttiä, joista on saatavissa korkealaatuista referenssiaineistoa, on olemassa vain vähän. Uusimmat menetelmät ja järjestelmät ovat usein vielä kehitysvaiheessa ja niiden käyttö onnistuu vain niiden kehittäjiltä. Korkealaatuisen testikentän tekeminen on työlästä, mutta kun testikenttä on perustettu, on helpompaa kerätä aineistoja siltä eri järjestelmillä kuin mitata referenssiaineistoa uusilta alueilta. Eri järjestelmien tai yhden järjestelmän eri asetuksien vertailu on helpompaa kun muuttujien määrä on mahdollisimman pieni; tässä tapauksessa kaukokartoitetut alueet pysyvät vakiona ja mahdolliset muutokset niissä ovat helpommin kontrolloitavissa. Vertailujen tulokset antavat hyödyllistä tietoa sekä kaukokartoitustuotteiden kehittäjille että niiden käyttäjille. Vertaillut kohdemallinnusmenetelmät olivat rakennusten ja yksittäisten puiden mallinnus yhteisiltä testikentiltä kerättyjä aineistoja käyttäen. Liikkuvien laserkeilausjärjestelmien suorituskykyä vertailtiin käyttäen perustetulta kaupunkitestikentältä kerättyjä aineistoja. Kaikissa tapauksissa todettiin että tärkein tuloksiin vaikuttava tekijä oli menetelmä tai järjestelmä itse, joten annetun mallinnus- tai kartoitustehtävän tulokset ovat hyvin vertailukelpoisia

ACCURACY EVALUATION OF A MOBILE MAPPING SYSTEM WITH ADVANCED STATISTICAL METHODS

This paper discusses a methodology to evaluate the precision and the accuracy of a commercial Mobile Mapping System (MMS) with advanced statistical methods. So far, the metric potentialities of this emerging mapping technology have been studied in few papers, where generally the assumption that errors follow a normal distribution is made. In fact, this hypothesis should be carefully verified in advance, in order to test how well the Gaussian classic statistics can adapt to datasets that are usually affected by asymmetrical gross errors. The workflow adopted in this study relies on a Gaussian assessment, followed by an outlier filtering process. Finally, non-parametric statistical models are applied, in order to achieve a robust estimation of the error dispersion. Among the different MMSs available on the market, the latest solution provided by RIEGL is here tested, i.e. the VMX-450 Mobile Laser Scanning System. The test-area is the historic city centre of Trento (Italy), selected in order to assess the system performance in dealing with a challenging and historic urban scenario. Reference measures are derived from photogrammetric and Terrestrial Laser Scanning (TLS) surveys. All datasets show a large lack of symmetry that leads to the conclusion that the standard normal parameters are not adequate to assess this type of data. The use of non-normal statistics gives thus a more appropriate description of the data and yields results that meet the quoted a-priori errors

Area-Based Approach for Mapping and Monitoring Riverine Vegetation Using Mobile Laser Scanning

Peer reviewe

Improving merge methods for grid-based digital elevation models

Digital Elevation Models (DEMs) are used to represent the terrain in applications such as, for example, overland flow modelling or viewshed analysis. DEMs generated from digitising contour lines or obtained by LiDAR or satellite data are now widely available. However, in some cases, the area of study is covered by more than one of the available elevation data sets. In these cases the relevant DEMs may need to be merged. The merged DEM must retain the most accurate elevation information available while generating consistent slopes and aspects. In this paper we present a thorough analysis of three conventional grid-based DEM merging methods that are available in commercial GIS software. These methods are evaluated for their applicability in merging DEMs and, based on evaluation results, a method for improving the merging of grid-based DEMs is proposed. DEMs generated by the proposed method, called Id:Blend, showed significant improvements when compared to DEMs produced by the three conventional methods in terms of elevation, slope and aspect accuracy, ensuring also smooth elevation transitions between the original DEMs. The results produced by the improved method are highly relevant different applications in terrain analysis, e.g., visibility, or spotting irregularities in landforms and for modelling terrain phenomena, such as overland flow