3D reconstruction with inverse depth filter of feature-based visual SLAM

doi:10.11947/j.AGCS.2019.20180421

Abstract

Abstract: Aiming at the problem that the current feature-based visual SLAM can only reconstruct a sparse point cloud and the ordinary frame does not contribute to point depth estimation, a novel 3D reconstruction method with inverse depth filter of feature-based visual SLAM is proposed, which utilizes video sequence to incrementally build a denser scene structure in real-time. Specifically, a motion model based keyframe tracking approach is designed to provide accurate relative pose relationship. The map point is no longer calculated directly by two-frame-triangulation, instead it is accumulated and updated by information of several frames with an inverse depth filter based on probability distribution. A back-end hybrid optimization framework composed of feature and direct method is introduced, as well as an adjustment constraint based point screening strategy, which can precisely and efficiently solve camera pose and structure. The experimental results demonstrate the superiority of proposed method on computational speed and pose estimation accuracy compared with existing methods. Meanwhile, it is shown that our method can reconstruct a denser globally consistent point cloud map.

Key words: visual simultaneous localization and mapping, 3D reconstruction, inverse depth filter, motion model, back-end hybrid optimization framework

CLC Number:

P231

ZHANG Yi, JIANG Ting, JIANG Gangwu, YU Anzhu, YU Ying. 3D reconstruction with inverse depth filter of feature-based visual SLAM[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(6): 708-717.

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