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Dense mapping for monocular-SLAM

Abstract : Simultaneous Localization and Mapping (SLAM) is the problem of constructing a 3D map while simultaneously keeping track of an agent location within the map. In recent years, work has focused in systems that use a single camera as the only sensing mechanism (monocular-SLAM). 3D reconstruction (map) by monocular-SLAM systems is a point cloud where all points preserve high accuracy and can deliver visual environmental information. However, the maximum number of points in the cloud is limited by the tracked features, this is named " sparse cloud problem ". In this work, we propose a new SLAM framework that is robust enough for indoor/outdoor SLAM applications, and at the same time increases the 3D map density. The point cloud density is increased by applying a new feature-tracking/dense-tracking algorithm in the SLAM formulation. In order to achieve real-time processing, the algorithm is formulated to facilitate a parallel FPGA implementation. Preliminary results show that it is possible to obtain dense mapping (superior to previous work) and accurate camera pose estimation (localization) under several real-world conditions.
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Contributor : Abiel Aguilar-González Connect in order to contact the contributor
Submitted on : Thursday, November 2, 2017 - 11:38:13 AM
Last modification on : Wednesday, February 24, 2021 - 4:16:01 PM
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Abiel Aguilar-González, Miguel Arias-Estrada. Dense mapping for monocular-SLAM. 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Oct 2016, Alcala de Henares, Spain. pp.1 - 8, ⟨10.1109/IPIN.2016.7743671⟩. ⟨hal-01627692⟩



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