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Aerial lidar point cloud voxelization with its 3D ground filtering application / Liying Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 2 (February 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 2 (February 2017) . - pp 95 - 107 Titre : Aerial lidar point cloud voxelization with its 3D ground filtering application Type de document : Article/Communication Auteurs : Liying Wang, Auteur ; Yan Xu, Auteur ; Yu Li, Auteur Année de publication : 2017 Article en page(s) : pp 95 - 107 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] adjacence [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] filtrage de points [Termes IGN] modèle conceptuel de données [Termes IGN] semis de points [Termes IGN] visualisation 3D [Termes IGN] voxel Résumé : (Auteur) Compared to raster grid, Triangulated Irregular Network (TIN), and point cloud, the benefit of voxel representation lies in that the implicit notion of adjacency and the true 3D representation can be presented simultaneously. A binary voxel-based data (BVD) model is proposed to reconstruct aerial lidar point cloud and based on the constructed model 3D ground filtering (V3GF) is developed for separating ground points from unground ones. The proposed V3GF algorithm selects the lowest voxels with a value of 1 as ground seeds and then labels them and their 3D connected set as ground voxels. The ISPRS benchmark dataset are used to compare the performance of V3GF with those of eight other publicized filtering methods. Results indicate that the V3GF improves on Axelsson's performance on five samples in terms of total error. The average Kappa coefficients for sites with relatively flat urban areas, rough slope and discontinuous surfaces are 92.49 percent, 72.23 percent and 61.27 percent, respectively. Numéro de notice : A2017-038 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.2.95 En ligne : https://doi.org/10.14358/PERS.83.2.95 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84139 [article]

GPS/BDS short-term ISB modelling and prediction / Nan Jiang in GPS solutions, vol 21 n° 1 (January 2017)  

Public [article]  inGPS solutions > vol 21 n° 1 (January 2017) . - pp 163 - 175 Titre : GPS/BDS short-term ISB modelling and prediction Type de document : Article/Communication Auteurs : Nan Jiang, Auteur ; Yan Xu, Auteur ; Tianhe Xu, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 163 - 175 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] analyse comparative [Termes IGN] données BeiDou [Termes IGN] données GNSS [Termes IGN] erreur systématique [Termes IGN] erreur systématique inter-systèmes [Termes IGN] méthode des moindres carrés [Termes IGN] modèle d'erreur [Termes IGN] orbitographie [Termes IGN] positionnement par BeiDou [Termes IGN] positionnement par GNSS [Termes IGN] positionnement ponctuel précis Résumé : (Auteur) The Chinese BeiDou Navigation Satellite System (BDS) has completed its first milestone by providing coverage of the Asia–Pacific area navigation service since December 27, 2012. With the combination of BDS, the GNSS precise point positioning (PPP) can improve its positioning accuracy, availability and reliability. However, in order to achieve the best positioning solutions, the inter-system bias (ISB) between GPS and BDS must be resolved as precisely as possible. In this study, a 1-week period (GPS week 1810) of GPS/BDS observations for 18 distributed stations from the International GNSS Service Multi-GNSS Experiment are processed. Primarily, the ISB is estimated by an extended Kalman filter as a piece-wise parameter every 30 min. Then we generate a smoothed ISB series (ISB_s) with a sliding window median filter to reject the outliers from the original estimated ISB series (ISB_o). After analysing the characteristics of the ISB_s, a short-term station-dependent ISB model based on a 1-week period is proposed in this study. This model consists of a quadratic polynomial in time and two or three periodic functions with diurnal and semi-diurnal periods. Frequency spectrum analysis is used to determine the periods of the periodic functions, and the coefficients of the quadratic function and the periodic functions are estimated by least squares. For model verification, we compare the ISB derived from the model (ISB_m) with ISB_s (assumed the true values). The comparisons indicate an almost normal distribution. It is found that the proposed model is consistent with the true values: the root-mean-square (RMS) values being about 0.7 ns, and some stations are even better. This means that the short-term ISB model proposed has a high fitting accuracy. Hence, it can be used for ISB prediction. Comparing the prediction ISB series (ISB_p) with ISB_s in the following week (GPS week 1811), we can draw the conclusion that the accuracy of the prediction declines with an increase in the time period. The 1-day period precision can achieve 0.57–1.21 ns, while the accuracy of the 2-day prediction decreases to 0.77–1.72 ns. Hence, we recommend a predicting duration of 1 day. The proposed model will be beneficial for subsequent GPS/BDS PPP or precise orbit determination (POD) since the ISB derived from this model can be considered as a priori constraint in the PPP/POD solutions. With this a priori constraint, the convergence time can be shortened by 19.6, 16.1 and 2.4 % in N, E and U components, respectively. The accuracy of result in the E component is remarkably improved by 11.9 %. Numéro de notice : A2017-015 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-015-0513-x Date de publication en ligne : 09/01/2016 En ligne : https://doi.org/10.1007/s10291-015-0513-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83946 [article]