Pyörivien monilaserkeilainjärjestelmien geometrinen kalibrointi

The introduction of light-weight and low-cost multi-beam laser scanners provides ample opportunities in positioning and mapping as well as automation and robotics. The fields of view (FOV) of these sensors can be further expanded by actuation, for example by rotation. These rotating multi-beam lidar (RMBL) systems can provide fast and expansive coverage of the geometries of spaces, but the nature of the sensors and their actuation leave room for improvement in accuracy and precision. Geometric calibration methods addressing this space have been proposed, and this thesis reviews a selection of these methods and evaluates their performance when applied to a set of data samples collected using a custom RMBL platform and six Velodyne multi-beam sensors (one VLP-16 Lite, four VLP-16s and one VLP-32C). The calibration algorithms under inspection are unsupervised and data-based, and they are quantitatively compared to a target-based calibration performed using a high-accuracy point cloud obtained using a terrestrial laser scanner as a reference. The data-based calibration methods are automatic plane detection and fitting, a method based on local planarity and a method based on the information-theoretic concept of information entropy. It is found that of these, the plane-fitting and entropy-based measures for point cloud quality obtain the best calibration results.Kevyet ja edulliset monilaserkeilaimet tuovat uusia mahdollisuuksia paikannus- ja kartoitusaloille mutta myös automaatioon ja robotiikkaan. Näiden sensorien näköaloja voidaan kasvattaa entisestään esimerkiksi pyörittämällä, ja näin toteutettavat pyörivät monilaserkeilainjärjestelmät tuottavat nopeasti kattavaa geometriaa niitä ympäröivistä tiloista. Sensorien rakenne ja järjestelmän liikkuvuus lisäävät kuitenkin kohinaa ja epävarmuutta mittauksissa, minkä vuoksi erilaisia geometrisia kalibrointimenetelmiä onkin ehdotettu aiemmassa tutkimuksessa. Tässä diplomityössä esitellään valikoituja kalibrointimenetelmiä ja arvioidaan niiden tuloksia koeasetelmassa, jossa pyörivälle alustalle asennetuilla Velodyne-monilaserkeilaimilla (yksi VLP-16 Lite, neljä VLP-16:aa ja yksi VLP-32C) mitataan liikuntasalin geometriaa. Tarkasteltavat menetelmät ovat valvomattomia ja vain mittauksiin perustuvia ja niitä verrataan samasta tilasta hankittuun tarkkaan maalaserkeilausaineistoon. Menetelmiä ovat tasojen automaattinen etsintä ja sovitus, paikalliseen tasomaisuuteen perustuva menetelmä sekä informaatioteoreettiseen entropiaan perustuva menetelmä. Näistä tasojen sovitus ja entropiamenetelmä saavuttivat parhaat kalibrointitulokset referenssikalibraatioon verrattaessa

Performance Assessment of Reference Modelling Methods for Defect Evaluation in Asphalt Concrete

The deterioration of road conditions and increasing repair deficits pose challenges for the maintenance of reliable road infrastructure, and thus threaten, for example, safety and the fluent flow of traffic. Improved and more efficient procedures for maintenance are required, and these require improved knowledge of road conditions, i.e., improved data. Three-dimensional mapping presents possibilities for large-scale collection of data on road surfaces and automatic evaluation of maintenance needs. However, the development and, specifically, evaluation of large-scale mobile methods requires reliable references. To evaluate possibilities for close-range, static, high-resolution, three-dimensional measurement of road surfaces for reference use, three measurement methods and five instrumentations are investigated: terrestrial laser scanning (TLS, Leica RTC360), photogrammetry using high-resolution professional-grade cameras (Nikon D800 and D810E), photogrammetry using an industrial camera (FLIR Grasshopper GS3-U3-120S6C-C), and structured-light handheld scanners Artec Leo and Faro Freestyle. High-resolution photogrammetry is established as reference based on laboratory measurements and point density. The instrumentations are compared against one another using cross-sections, point-point distances, and ability to obtain key metrics of defects, and a qualitative assessment of the processing procedures for each is carried out. It is found that photogrammetric models provide the highest resolutions (10-50 million points per m2) and photogrammetric and TLS approaches perform robustly in precision with consistent sub-millimeter offsets relative to one another, while handheld scanners perform relatively inconsistently. A discussion on the practical implications of using each of the examined instrumentations is presented

Tundrakasviyhteisöjen tilallisen rakenteen havainnointi multi- ja hyperspektrikuvin

Arktisen alueen ekosysteemien yhteisörakenne on suuren muutoksen edessä lämpenemisen, varvuuntumisen, ikiroudan sulamisen ja muiden ympäristömuutosten myötä. Näiden ekosysteemien tilallisen heterogeenisyyden takia tällaisten muutosten paikallisen tason ymmärtäminen vaatii korkean resoluution aineistoa. Satelliitti- ja ilmakuvat ovat muodostuneet vakiomenetelmäksi laajojen alueiden ja rakenteiden kartoittamisessa. Sensoriteknologian kehitys, miehittämättömien lennokkien leviäminen sekä prosessorien tehostuminen mahdollistavat korkeampien spatiaali- ja spektraaliresoluutioiden käytön. Näin ollen tarkempia ekologisia havaintoja voidaan tehdä kaukokartoitusmenetelmin Tässä tutkielmassa arvioin kuinka kasvava spektraaliresoluutio vaikuttaa matalakasvuisin tunturinummen kasviyhteisöjen kaukokartoitukseen perustuvaan mallinnukseen. Laajaan kenttäaineistoon pohjaten arvioin biomassaa, lehtipinta-alaa, lajien lukumäärää, Shannonin biodiversiteetti-indeksiä sekä sumeita yhteisöklustereita. Mallinnan nämä satunnaisilla metsillä käyttäen eri spektraali-, spatiaali- ja ajallisten ominaisuuksien kuva-aineistoja sekä topografista aineistoa. Lopuksi yleistän mallit kasvillisuuskartoiksi. Lehtipinta-ala ja biomassa ennustuvat parhaiten 0,64 ja 0,59 selitysasteilla, ja multispektriaineisto on tärkein selittäjä. Biodiversiteettimuuttujat selittyvät parhaiten 0,40–0,50 selitysasteilla niin, että tärkeimmät selittävät muuttujat ovat topografisia ja hyperspektrisiä. Yhteisöklustereiden kuuluvuusarvojen selitysasteet ovat parhaimmillaan 0,27–0,53, ja tärkeimmät selittävät muuttujat vaihtelevat yhteisöklusterista toiseen. Näitä tuloksia soveltaen voi arvioida eri korkean resoluution kaukokartoitusmenelmiä vastaavien kasviyhteisöjen mallintamiseen.Arctic ecosystems face drastic changes in community structure due to warming, shrubification, permafrost loss, and other environmental changes. Due to the spatial heterogeneity of these ecosystems, understanding such changes on a local scale requires high-resolution data. Earth observation using satellite imagery and aerial photography has become a staple in mapping large areas and general patterns. Advances in sensor technology, the proliferation of unmanned aerial vehicles (UAVs), and increases in processing capacity enable the use of higher spatial and spectral resolutions. As a result, more detailed ecological observations can be made using remote sensing methods. In this thesis, I assess how increased spectral resolution affects the remote-sensing based modelling of plant communities in low-growth oroarctic tundra heaths. Based on a large field observation dataset, I estimate biomass, leaf area index, species richness, Shannon's biodiversity index, and fuzzy community clusters. I then build random forest models of these with image data of varying spectral, spatial, and temporal specifications and topographical data. Finally, I create maps of the vegetation. Leaf area index and biomass are best estimated of the response variables, with R2 values of 0.64 and 0.59, respectively, with multispectral data proving the most important explanatory dataset. Biodiversity metrics are best estimated with R2 values of 0.40–0.50 with the most important explanatory variables being topographical and hyperspectral, and community cluster with R2 values of 0.27–0.53, with the importance of various explanatory variables depending on the cluster being estimated. These results can help choose a suitable high-resolution remote sensing approach for modelling plant communities in similar conditions

Performance assessment of reference modelling methods for defect evaluation in asphalt concrete

Funding Information: Funding: This research was funded by the Academy of Finland (AoF) through the “Road Distress Mapping Combining Expertise of Sensors and Point Cloud Processing, Surveying and Road Engineering” project (decisions 323783, 323752, and 323750) and the Strategic Research Council “Competence-Based Growth Through Integrated Disruptive Technologies of 3D Digitalization, Robotics, Geospatial Information and Image Processing/Computing—Point Cloud Ecosystem” project (decisions 293389 and 314312) and project Profi5 “Autonomous systems” (No. 326246). In addition, the work was supported partly under AoF flagship project UNITE—Forest-Human-Machine Interplay (337656) and Lidar-based energy efficient ICT solutions (319011). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.The deterioration of road conditions and increasing repair deficits pose challenges for the maintenance of reliable road infrastructure, and thus threaten, for example, safety and the fluent flow of traffic. Improved and more efficient procedures for maintenance are required, and these require improved knowledge of road conditions, i.e., improved data. Three-dimensional mapping presents possibilities for large-scale collection of data on road surfaces and automatic evaluation of maintenance needs. However, the development and, specifically, evaluation of large-scale mobile methods requires reliable references. To evaluate possibilities for close-range, static, high-resolution, three-dimensional measurement of road surfaces for reference use, three measurement methods and five instrumentations are investigated: terrestrial laser scanning (TLS, Leica RTC360), photogrammetry using high-resolution professional-grade cameras (Nikon D800 and D810E), photogrammetry using an industrial camera (FLIR Grasshopper GS3-U3-120S6C-C), and structured-light handheld scanners Artec Leo and Faro Freestyle. High-resolution photogrammetry is established as reference based on laboratory measurements and point density. The instrumentations are compared against one another using cross-sections, point–point distances, and ability to obtain key metrics of defects, and a qualita-tive assessment of the processing procedures for each is carried out. It is found that photogrammetric models provide the highest resolutions (10–50 million points per m2) and photogrammetric and TLS approaches perform robustly in precision with consistent sub-millimeter offsets relative to one another, while handheld scanners perform relatively inconsistently. A discussion on the practical implications of using each of the examined instrumentations is presented.Peer reviewe