Auteurs

Daniel, Alison; Pierre, Aubry; Arshid, Azarine; Gilles, Barone-Rochette; Pierre-Antoine, Barral; Bartoli, Axel; Ahmed, Ben Driss; Bière, Loïc; Boccalini, Sara; Jacques, Boschat; Louis, Boussel; Brochet, Eric; Cadour, Farah; Cammilleri, Serge; Lucie, Cassagnes; Caussin, Christophe; Charpentier, Etienne; Naima, Chergui; Christiaens, L; Guillaume, Clerfond; Cochet, Hubert; Cohen-Solal, A; Dominique, Crochet; Pierre, Croisille; Jean-Nicolas, Dacher; Deux, Jean-François; Didier, Romain; Benoit, Diebold; Chloé, Dorczynski; Douek, Philippe; Fabrice, Extramania; Jean, Ferrières; Alain, Furber; Gabriel, Garcia; Jérôme, Garot; Olivier, Genée; Olivier, Ghekiere; Nil, Ghelis; Martine, Gilard; Habib, Gilbert; Michèle, Hamon; Thomas, Hebert; Fabien, Hyafil; Alexis, Jacquier; Bruno, Kastler; Lairez, Olivier; Laissy, Jean-Pierre; Antoine, Larralde; Florent, Le Ven; Laurent, Macron; Mandry, Damien; Nathan, Mewton; Élie, Mousseaux; Nicol, Martin; Mickaël, Ohana; Ou, Phalla; Chirine, Parsaï; Antoine, Petermann; Théo, Pezel; François, Pontana; Julien, Pucheux; Stanislas, Rapacchi; Alban, Redheuil; Paul, Revel Mouroz; Sophie, Ribeyrolles; Rousseau, Hervé; Serfaty, Jean-Michel; Salim, Si-Mohamed; Marc, Sirol; Gilles, Soulat; Soumaya, Sridi-Cheniti; Varroud-Vial, Nicolas; Emmanuelle, Vermes; Hélène, Vernhet-Kovacsik; Karine, Warin-Fresse; Willoteaux, S.

doi:10.1016/b978-2-294-76723-4.09990-6

Cited by 1 publication

(1 citation statement)

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“…[133] use several rounds of visual-inertial bundle adjustment to make a robust initialization for VIO. Specially, tango [134], Dyson 360 Eye and hololens [135] are the real products of VIO and receive good feedback. In addition to this, ARkit (filter-based) from Apple, ARcore (filter-based) from Google, Inside-out from uSens are the technology of VIO.…”

Section: Visual Inertial Odometry Slammentioning

confidence: 99%

A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks

Huang,

Zhao,

Luo

et al. 2021

JGPS

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Simultaneous Localization and Mapping (SLAM) achieves the purpose of simultaneous positioning and map construction based on self-perception. The paper makes an overview in SLAM including Lidar SLAM, visual SLAM, and their fusion. For Lidar or visual SLAM, the survey illustrates the basic type and product of sensors, open source system in sort and history, deep learning embedded, the challenge and future. Additionally, visual inertial odometry is supplemented. For Lidar and visual fused SLAM, the paper highlights the multi-sensors calibration, the fusion in hardware, data, task layer. The open question and an envision in 6G wieless networks with SLAM end the paper. The contributions of this paper can be summarized as follows: the paper provides a high quality and full-scale overview in SLAM. It's very friendly for new researchers to hold the development of SLAM and learn it very obviously. Also, the paper can be considered as dictionary for experienced researchers to search and find new interested orientation.

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Section: Visual Inertial Odometry Slammentioning

confidence: 99%