An Analytical Solution to the IMU Initialization Problem for Visual-Inertial Systems
An Analytical Solution to the IMU Initialization Problem for Visual-Inertial Systems
复制标题
视觉惯性系统IMU初始化问题的解析解
DOI：
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发表时间：
2021
影响因子：
5.2
通讯作者：
J. Gonzalez
中科院分区：
文献类型：
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作者：
David Zuñiga;F. Moreno;J. Gonzalez
视觉和惯性测量的融合在机器人社区中变得越来越流行，因为这两种信息来源可以很好地互补。然而，为了执行这种融合，必须首先初始化惯性测量单元（IMU）的偏差以及重力方向。如果是单目相机，还需要公制刻度。最流行的视觉惯性初始化方法依赖于精确的仅视觉运动估计来构建非线性优化问题，以迭代方式求解这些参数。在这封信中，我们依靠[1]中之前的工作，提出了一种解析解来估计最大后验框架中的加速度计偏差、重力方向和比例因子。这种公式产生了一种非常有效的估计方法，并且由于解决方案的非迭代性质，避免了先前迭代解决方案的固有问题。我们对所提出的 IMU 初始化方法进行了广泛的验证，并使用公开的 EuRoC 数据集中的真实数据与 [2] 和 [3] 中描述的最先进方法进行了性能比较。我们的方法无需对比例因子进行初始猜测即可实现更高的精度，并且结合了加速度计偏差的先验以避免可观测性问题。就计算效率而言，它与第一个工作一样快，比第二个工作快两倍。我们还提供了 C++ 开源参考实现。
The fusion of visual and inertial measurements is becoming more and more popular in the robotics community since both sources of information complement each other well. However, in order to perform this fusion, the biases of the Inertial Measurement Unit (IMU) as well as the direction of gravity must be initialized first. In case of a monocular camera, the metric scale is also needed. The most popular visual-inertial initialization approaches rely on accurate vision-only motion estimates to build a non-linear optimization problem that solves for these parameters in an iterative way. In this letter, we rely on the previous work in [1] and propose an analytical solution to estimate the accelerometer bias, the direction of gravity and the scale factor in a maximum-a-posteriori framework. This formulation results in a very efficient estimation approach and, due to the non-iterative nature of the solution, avoids the intrinsic issues of previous iterative solutions. We present an extensive validation of the proposed IMU initialization approach and a performance comparison against the state-of-the-art approaches described in [2] and [3] with real data from the publicly available EuRoC dataset. Our approach achieves better accuracy without requiring an initial guess for the scale factor and incorporates a prior for the accelerometer bias in order to avoid observability issues. In terms of computational efficiency, it is as fast as the first work and two times faster than the second. We also provide a C++ open source reference implementation.