Preregistration Classification of Mobile LIDAR Data Using Spatial Correlations

We explore a novel paradigm for light detection and ranging (LIDAR) point classification in mobile laser scanning (MLS). In contrast to the traditional scheme of performing classification for a 3-D point cloud after registration, our algorithm operates on the raw data stream classifying the points on-the-fly before registration. Hence, we call it preregistration classification (PRC). Specifically, this technique is based on spatial correlations, i.e., local range measurements supporting each other. The proposed method is general since exact scanner pose information is not required, nor is any radiometric calibration needed. Also, we show that the method can be applied in different environments by adjusting two control parameters, without the results being overly sensitive to this adjustment. As results, we present classification of points from an urban environment where noise, ground, buildings, and vegetation are distinguished from each other, and points from the forest where tree stems and ground are classified from the other points. As computations are efficient and done with a minimal cache, the proposed methods enable new on-chip deployable algorithmic solutions. Broader benefits from the spatial correlations and the computational efficiency of the PRC scheme are likely to be gained in several online and offline applications. These range from single robotic platform operations including simultaneous localization and mapping (SLAM) algorithms to wall-clock time savings in geoinformation industry. Finally, PRC is especially attractive for continuous-beam and solid-state LIDARs that are prone to output noisy data

Accurate derivation of stem curve and volume using backpack mobile laser scanning

Forest inventories rely on field plots, the measurement of which is costly and time consuming by manual means. Thus, there is a need to automate plot-level field data collection. Mobile laser scanning has yet to be demonstrated for deriving stem curve and volume from standing trees with sufficient accuracy for supporting forest inventory needs. We tested a new approach based on pulse-based backpack mobile laser scanner (Riegl VUX-1HA) combined with in-house developed SLAM (Simultaneous Localization and Mapping), and a novel post-processing algorithm chain that allows one to extract stem curves from scan-line arcs corresponding to individual standing trees. The post-processing step included, among others, an algorithm for scan-line arc extraction, a stem inclination angle correction and an arc matching algorithm correcting for the drifts that are still present in the stem points after applying the SLAM algorithm. By using the stem curves defined by the detected arcs and tree heights provided by the pulse-based scanner, stem volume estimates for standing trees in easy (n = 40) and medium (n = 37) difficult boreal forest were calculated. In the easy and medium plots, 100% of pine and birch stems were correctly detected. The total RMSE of the extracted stem curves was 1.2 cm (5.1%) and 1.7 cm (6.7%) for the easy and medium plots, respectively. The RMSE were 1.8 m (8.7%) and 1.1 m (4.9%) for the estimated tree heights, and 9.7% and 10.9% for the stem volumes for the easy and medium plots, correspondingly. Thus, our processing chain provided stem volume estimates with a better accuracy than previous methods based on mobile laser scanning data. Importantly, the accuracy of stem volume estimation was comparable to that provided by terrestrial laser scanning approaches in similar forest conditions. To further demonstrate the performance of the proposed method, we compared our results against stem volumes calculated using the standard Finnish allometric volume model, and found that our method provided more accurate volume estimates for the two test sites. The findings are important steps towards future individual-tree-based airborne laser scanning inventories which currently lack cost-efficient and accurate field reference data collection techniques. The tree geometry defined by the stem curve is also an important input parameter for deriving quality-related information from trees. Forest management decision making will benefit from improvements to the efficiency and quality of individual tree reference information.</p

PRECISE INDOOR LOCALIZATION FOR MOBILE LASER SCANNER

Accurate 3D data is of high importance for indoor modeling for various applications in construction, engineering and cultural heritage documentation. For the lack of GNSS signals hampers use of kinematic platforms indoors, TLS is currently the most accurate and precise method for collecting such a data. Due to its static single view point data collection, excessive time and data redundancy are needed for integrity and coverage of data. However, localization methods with affordable scanners are used for solving mobile platform pose problem. The aim of this study was to investigate what level of trajectory accuracies can be achieved with high quality sensors and freely available state of the art planar SLAM algorithms, and how well this trajectory translates to a point cloud collected with a secondary scanner. In this study high precision laser scanners were used with a novel way to combine the strengths of two SLAM algorithms into functional method for precise localization. We collected five datasets using Slammer platform with two laser scanners, and processed them with altogether 20 different parameter sets. The results were validated against TLS reference. The results show increasing scan frequency improves the trajectory, reaching 20 mm RMSE levels for the best performing parameter sets. Further analysis of the 3D point cloud showed good agreement with TLS reference with 17 mm positional RMSE. With precision scanners the obtained point cloud allows for high level of detail data for indoor modeling with accuracies close to TLS at best with vastly improved data collection efficiency

Patient exposure levels in radiotherapy CT simulations in Finland

POTILAIDEN SÄTEILYALTISTUSTASOT SÄDEHOIDON TT-SIMULOINNEISSA SUOMESSA Tietokonetomografialla (TT) toteutettu simulointi on olennainen osa sädehoitoprosessia. Koska säteilyaltistus kohdistuu hoidettavan alueen lisäksi sitä ympäröiviin terveisiin kudoksiin, pitää sädehoidon TT-simulaattoreilla käytettävä annostaso optimoida. Annostaso ja kuvausparametrit pitää valita siten, että kuvasta saadaan riittävä tieto hyvän hoitotuloksen saavuttamiseksi. Diagnostisessa kuvantamisessa käytettyjä STUKin antamia kansallisia vertailutasoja ei voida suoraan käyttää sädehoidon TT-simuloinnin optimoinnissa. Kansallisten simulointikäytäntöjen kartoittamiseksi kokosimme tietoa käytössä olevista kuvausarvoista ja annoksista. Tiedot kerättiin neljän tyypillisen sädehoitokohteen TT-simuloinneista: resektiorinta, eturauhanen, koko aivot ja kaulan alue. Tiedot saatiin kaikista Suomen kolmestatoista sädehoitokeskuksesta ja yhteensä 15 laitteelle. Käytetyissä annostasoissa ja kuvausparametreissa oli suurta vaihtelua. Vartalon alueen tutkimuksissa keskimääräiset TT-annosindeksit ja niiden standardihajonnat eri TT-laitteiden välillä olivat eturauhaselle: 24 mGy (53%), resektiorinnalle: 18 mGy (54%), ja kaulan alueella: 29 mGy (35%), kun diagnostiikan vertailutaso vartalon alueella on 12 mGy. Pään kuvauksissa arvot olivat 70 mGy (35%), kun vastaava diagnostiikan vertailutaso on 55 mGy. Keskimääräiset annostasot olivat siis selkeästi suurempia, kuin mitä käytetään diagnostisessa kuvantamisessa. Selvityksen perusteella annostaso ei suoraan liittynyt esim. laitetyyppiin, valittuun leikepaksuuteen tai leikeväleihin. Jokaisen sädehoitokohteen simuloi vähintään yksi sädehoitokeskus diagnostiikkaa vastaavalla annostasolla ja saavutettu kuvanlaatutaso koettiin niissä riittäväksi. Tämän perusteella voidaan todeta, että TT-simuloinnin annostasoissa ja kuvausarvoissa olisi optimoitavaa. Selvityksen tuloksista keskusteltiin kansallisesti sädehoitofyysikoiden neuvottelupäivillä ja sädehoitokeskuksille annettiin yksilöllistä palautetta niiden annostasoista ja kuvausparametreista suhteessa muihin sädehoitokeskuksiin. Tässä selvityksessä saatuja tuloksia käytetään valvonnan tukena arvioitaessa optimoinnin toteutumista

Accuracy of kinematic positioning using global satellite navigation systems under forest canopies

A harvester enables detailed roundwood data to be collected during harvesting operations by means of the measurement apparatus integrated into its felling head. These data can be used to improve the efficiency of wood procurement and also replace some of the field measurements, and thus provide both less costly and more detailed ground truth for remote sensing based forest inventories. However, the positional accuracy of harvester-collected tree data is not sufficient currently to match the accuracy per individual trees achieved with remote sensing data. The aim in the present study was to test the accuracy of various instruments utilizing global satellite navigation systems (GNSS) in motion under forest canopies of varying densities to enable us to get an understanding of the current state-of-the-art in GNSS-based positioning under forest canopies. Tests were conducted using several different combinations of GNSS and inertial measurement unit (IMU) mounted on an all-terrain vehicle (ATV) simulating a moving harvester. The positions of 224 trees along the driving route were measured using a total-station and real-time kinematic GPS. These trees were used as reference items. The position of the ATV was obtained using GNSS and IMU with an accuracy of 0.7 m (root mean squared error (RMSE) for 2D positions). For the single-frequency GNSS receivers, the RMSE of real-time 2D GNSS positions was 4.2-9.3 m. Based on these results, it seems that the accuracy of novel single-frequency GNSS devices is not so dependent on forest conditions, whereas the performance of the tested geodetic dual-frequency receiver is very sensitive to the visibility of the satellites. When post-processing can be applied, especially when combined with IMU data, the improvement in the accuracy of the dual-frequency receiver was significant.Peer reviewe