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Conference Papers Year : 2021

TT-SLAM: Dense Monocular SLAM for Planar Environments

Abstract

This paper proposes a novel visual SLAM method with dense planar reconstruction using a monocular camera: TT-SLAM. The method exploits planar template-based trackers (TT) to compute camera poses and reconstructs a multiplanar scene representation. Multiple homographies are estimated simultaneously by clustering a set of template trackers supported by superpixelized regions. Compared to RANSACbased multiple homographies method [1], data association and keyframe selection issues are handled by the continuous nature of template trackers. A non-linear optimization process is applied to all the homographies to improve the precision in pose estimation. Experiments show that the proposed method outperforms RANSAC-based multiple homographies method [1] as well as other dense method SLAM techniques such as LSD-SLAM or DPPTAM, and competes with keypointbased techniques like ORB-SLAM while providing dense planar reconstructions of the environment.
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Dates and versions

hal-03169199 , version 1 (15-03-2021)

Identifiers

  • HAL Id : hal-03169199 , version 1

Cite

Xi Wang, Marc Christie, Eric Marchand. TT-SLAM: Dense Monocular SLAM for Planar Environments. ICRA 2021 - IEEE International Conference on Robotics and Automation, May 2021, Xi'an, China. pp.11690-11696. ⟨hal-03169199⟩
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