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Boresight calibration of low point density Lidar sensors / Sudhagar Nagarajan in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 10 (October 2018)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 10 (October 2018) . - pp 619 - 627 Titre : Boresight calibration of low point density Lidar sensors Type de document : Article/Communication Auteurs : Sudhagar Nagarajan, Auteur ; Shahram Moafipoor, Auteur Année de publication : 2018 Article en page(s) : pp 619 - 627 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] étalonnage d'instrument [Termes IGN] géoréférencement direct [Termes IGN] ligne de visée [Termes IGN] plan (géométrie) [Termes IGN] semis de points Résumé : (Auteur) Mobile Mapping is the technique of acquiring accurate geospatial information of a scene using multiple sensors mounted on a moving platform. At the core of these systems is the direct georeferencing techniques that tie together multi-sensor data on-board. An important aspect of direct georeferencing is to apply accurate boresight calibration of individual sensors with respect to the platform body frame. Conventional techniques use Ground Control Points (GCP) for this calibration. Considering the challenges in identifying GCPs from low density lidar point cloud, this research presents a feature-based registration method that uses control planes. The presented method is performed in a lab-facility utilizing static data to determine the alignment between platform body frame and lidar frame by minimizing the volume formed between low point density lidar and control planes. The paper discusses the mathematical models and feasibility of the technique for use in mapping applications. Numéro de notice : A2018-430 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.10.619 Date de publication en ligne : 01/10/2018 En ligne : https://doi.org/10.14358/PERS.84.10.619 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90988 [article]

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Registration of images to Lidar and GIS data without establishing explicit correspondences / Gabor Barsai in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 10 (October 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 10 (October 2017) . - pp 705 - 716 Titre : Registration of images to Lidar and GIS data without establishing explicit correspondences Type de document : Article/Communication Auteurs : Gabor Barsai, Auteur ; Alper Yilmaz, Auteur ; Sudhagar Nagarajan, Auteur ; Panu Srestasathiern, Auteur Année de publication : 2017 Article en page(s) : pp 705 - 716 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] contour [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion d'images [Termes IGN] image aérienne oblique [Termes IGN] méthode de réduction d'énergie [Termes IGN] superposition d'images [Termes IGN] variable aléatoire Résumé : (auteur) Recovering the camera orientation is a fundamental problem in photogrammetry for precision 3D recovery, orthophoto generation, and image registration. In this paper, we achieve this goal by fusing the image information with information extracted from different modalities, including lidar and GIS. In contrast to other approaches, which require feature correspondences, our approach exploits edges across the modalities without the necessity to explicitly establish correspondences. In the proposed approach, extracted edges from different modalities are not required to have analytical forms. This flexibility is achieved by minimizing a new cost function using a Bayesian approach, which takes the Euclidean distances between the projected edges extracted from the other data source and the edges extracted from the reference image as its random variable. The proposed formulation minimizes the overall distances between the sets of edges iteratively, such that the end product results in the correct camera parameters for the reference image as well as matching features across the modalities. The initial solution can be obtained from GPS/IMU data. The formulation is shown to successfully handle noise and missing observations in edges. Point matching methods may fail for oblique images, especially high oblique images. We eliminate the requirement for exact point-to-point matching. The feasibility of the method is experimented with nadir and oblique images. Numéro de notice : A2017-691 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.10.705 En ligne : https://doi.org/10.14358/PERS.83.10.705 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87858 [article]