Revisiting visual-inertial structure from motion for odometry and SLAM initialization

• URL: http://arxiv.org/abs/2006.06017v2
• Date: Thu, 28 Jan 2021 17:58:46 GMT
• Title: Revisiting visual-inertial structure from motion for odometry and SLAM initialization
• Authors: Georgios Evangelidis, Branislav Micusik
• Abstract summary: We build on a direct triangulation of the unknown $3D$ point paired with each of its observations. All the observations of every scene point are jointly related, thereby leading to a less biased and more robust solution. The proposed formulation attains up to $50$ percent decreased velocity and point reconstruction error compared to the standard closed-form solver.
• Score: 5.33024001730262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, an efficient closed-form solution for the state initialization in visual-inertial odometry (VIO) and simultaneous localization and mapping (SLAM) is presented. Unlike the state-of-the-art, we do not derive linear equations from triangulating pairs of point observations. Instead, we build on a direct triangulation of the unknown $3D$ point paired with each of its observations. We show and validate the high impact of such a simple difference. The resulting linear system has a simpler structure and the solution through analytic elimination only requires solving a $6\times 6$ linear system (or $9 \times 9$ when accelerometer bias is included). In addition, all the observations of every scene point are jointly related, thereby leading to a less biased and more robust solution. The proposed formulation attains up to $50$ percent decreased velocity and point reconstruction error compared to the standard closed-form solver, while it is $4\times$ faster for a $7$-frame set. Apart from the inherent efficiency, fewer iterations are needed by any further non-linear refinement thanks to better parameter initialization. In this context, we provide the analytic Jacobians for a non-linear optimizer that optionally refines the initial parameters. The superior performance of the proposed solver is established by quantitative comparisons with the state-of-the-art solver.

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