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TT-SLAM: Dense Monocular SLAM for Planar Environments

Xi Wang 1 Marc Christie 1 Eric Marchand 2
1 MIMETIC - Analysis-Synthesis Approach for Virtual Human Simulation
UR2 - Université de Rennes 2, Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
2 RAINBOW - Sensor-based and interactive robotics
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
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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https://hal.inria.fr/hal-03169199
Contributor : Eric Marchand <>
Submitted on : Monday, March 15, 2021 - 10:50:04 AM
Last modification on : Thursday, March 18, 2021 - 11:56:41 AM

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  • HAL Id : hal-03169199, version 1

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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.1-7. ⟨hal-03169199⟩

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