论文标题
从运动和猛击初始化的运动中重新访问视觉惯性结构
Revisiting visual-inertial structure from motion for odometry and SLAM initialization
论文作者
论文摘要
在本文中，提出了一种有效的封闭式解决方案，用于在视觉惯性探光仪（VIO）（VIO）和同时定位和映射（SLAM）中的状态初始化解决方案。与最先进的情况不同，我们不会从三角观测对中得出线性方程。取而代之的是，我们基于对未知$ 3D $点的直接三角剖分与每个观察结果配对。我们显示并验证了如此简单的差异的高影响。所得的线性系统具有更简单的结构，并且通过分析消除的解决方案仅需要求解$ 6 \ times 6 $线性系统（或包括加速度计偏置时$ 9 \ times 9 $）。此外，每个场景点的所有观察结果都是共同相关的，从而导致偏见和更强大的解决方案。与标准的封闭式求解器相比，拟议的配方可达到高达50美元的速度和点重建误差，而$ 4 \ times $ $ \ times $ $ \ times $ $ \ times $更快。除了固有的效率外，由于更好的参数初始化，任何进一步的非线性精炼需要更少的迭代。在这种情况下，我们为非线性优化器提供了分析性雅各布人，该优化器可选地完善初始参数。通过与最先进的求解器进行定量比较来确定所提出的求解器的出色性能。
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.
