English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/220034
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:


VPS-SLAM: Visual Planar Semantic SLAM for Aerial Robotic Systems

AuthorsBavle, H.; De La Puente, P.; How, J. P.; Campoy, P.
Visual SLAM
Visual semantic SLAM
Autonomous aerial robots
Three-dimensional displays
Issue Date24-Mar-2020
PublisherInstitute of Electrical and Electronics Engineers
CitationIEEE Access 8: 60704 - 60718 (2020)
AbstractIndoor environments have abundant presence of high-level semantic information which can provide a better understanding of the environment for robots to improve the uncertainty in their pose estimate. Although semantic information has proved to be useful, there are several challenges faced by the research community to accurately perceive, extract and utilize such semantic information from the environment. In order to address these challenges, in this paper we present a lightweight and real-time visual semantic SLAM framework running on board aerial robotic platforms. This novel method combines low-level visual/visual-inertial odometry (VO/VIO) along with geometrical information corresponding to planar surfaces extracted from detected semantic objects. Extracting the planar surfaces from selected semantic objects provides enhanced robustness and makes it possible to precisely improve the metric estimates rapidly, simultaneously generalizing to several object instances irrespective of their shape and size. Our graph-based approach can integrate several state of the art VO/VIO algorithms along with the state of the art object detectors in order to estimate the complete 6DoF pose of the robot while simultaneously creating a sparse semantic map of the environment. No prior knowledge of the objects is required, which is a significant advantage over other works. We test our approach on a standard RGB-D dataset comparing its performance with the state of the art SLAM algorithms. We also perform several challenging indoor experiments validating our approach in presence of distinct environmental conditions and furthermore test it on board an aerial robot.
Publisher version (URL)https://doi.org/10.1109/ACCESS.2020.2983121
Appears in Collections:(CAR) Artículos
Files in This Item:
File Description SizeFormat 
VPS-SLAM_Visual_Planar_Semantic_SLAM.pdfArtículo principal4,17 MBAdobe PDFThumbnail
Show full item record
Review this work

Related articles:

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.