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Constrained RGBD-SLAM

Published online by Cambridge University Press:  02 June 2020

Sylvie Naudet-Collette Open the ORCID record for Sylvie Naudet-Collette [Opens in a new window] ,
Kathia Melbouci ,
Vincent Gay-Bellile ,
Omar Ait-Aider  and
Michel Dhome
Sylvie Naudet-Collette*
Affiliation:
CEA LIST, DRT/LIST/DIASI/SIALV, DRT/LIST/DIASI/SIALV, CEA Saclay Nano-INNOV , Gif-sur-Yvette, 91191
Kathia Melbouci
Affiliation:
CEA, LIST, Artificial intelligence Language and Vision Laboratory, F-91191 Gif-sur-Yvette, France, E-mails: [email protected], [email protected]
Vincent Gay-Bellile
Affiliation:
CEA, LIST, Artificial intelligence Language and Vision Laboratory, F-91191 Gif-sur-Yvette, France, E-mails: [email protected], [email protected]
Omar Ait-Aider
Affiliation:
Pascal Institut, UMR 660, Blaise Pascal University, 63000 Clermont Ferrand, France, E-mails: [email protected], [email protected]
Michel Dhome
Affiliation:
Pascal Institut, UMR 660, Blaise Pascal University, 63000 Clermont Ferrand, France, E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper introduces a new RGBD-Simultaneous Localization And Mapping (RGBD-SLAM) based on a revisited keyframe SLAM. This solution improves the localization by combining visual and depth data in a local bundle adjustment. Then, it presents an extension of this RGBD-SLAM that takes advantage of a partial knowledge of the scene. This solution allows using a prior knowledge of the 3D model of the environment when this latter is available which drastically improves the localization accuracy. The proposed solutions called RGBD-SLAM and Constrained RGBD-SLAM are evaluated on several public benchmark datasets and on real scenes acquired by a Kinect sensor. The system works in real time on a standard central processing units and it can be useful for certain applications, such as localization of lightweight robots, UAVs, and VR helmet.

Keywords

Simultaneous localization and mappingRGBD sensorBundle adjustment3D modelRGBD-SLAM
Type
Articles
Information
Robotica , Volume 39 , Issue 2 , February 2021 , pp. 277 - 290
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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