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Point cloud refinement with self-calibration of a mobile multibeam lidar sensor

Abstract : Lidar sensors are widely used in mobile mapping systems. With recent developments, these sensors provide large volumes of data which are necessary for some applications that require a high level of detail. Multibeam lidar sensors can provide this level of detail, but need a specific calibration routine to provide the best precision possible. Because they have multiple beams, the calibration of such sensors is difficult and is not well represented in the literature. This work presents an automatic method for the optimisation of the calibration parameters of a multibeam lidar sensor mounted on a mobile platform. The proposed approach does not require any calibration target, and only uses information from the acquired point clouds, which makes it simple to use. The goal of the optimisation is to find calibration parameters that will improve the structure of the data. At the end of the automatic process, a confidence value is provided for the calibration parameters found.
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Contributeur : Jean-Emmanuel Deschaud <>
Soumis le : jeudi 15 février 2018 - 09:18:50
Dernière modification le : mardi 7 janvier 2020 - 01:22:38
Document(s) archivé(s) le : mercredi 2 mai 2018 - 14:57:59


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Houssem Nouira, Jean-Emmanuel Deschaud, François Goulette. Point cloud refinement with self-calibration of a mobile multibeam lidar sensor. Photogrammetric Record, Wiley, 2017, 32 (159), pp.291 - 316. ⟨10.1111/phor.12198⟩. ⟨hal-01695870⟩



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