Initialization is a fundamental task for the state estimation of multi-sensor integration. In this paper, we propose a novel approach to achieve a fast and accurate initialization for the integrated system. With the outputs from GNSS, the rough roll, pitch, yaw, and direction of gravity are calculated. Based on these initial states, the GNSS/INS fusion and Visual-Inertial Odometry (VIO) are simultaneously launched whenever the observations are available. Subsequently, a coarse scale is retrieved by assigning the initial states of GNSS/INS fusion to the VIO procedure. The scale can be utilized to get poses and points very close to the real world. Once the VIO estimation converges, the transformation parameters between the poses of VIO and GNSS/INS fusion are estimated via non-linear optimization. Thus, we can align these two trajectories and evaluate the scale error. Through six vehicular tests, we analyze the performance of the proposed method with different illumination, obstructions, and motion patterns, and compare the results of the proposed approach with the state-of-the-art algorithms. The results indicated that the Visual-Inertial parameters converge within 9 seconds in our method. The scale errors of both our method are mostly within 10%. The alignment errors are mostly at centimeter-level in our method, which is comparable to the existing approaches.

中文翻译：

---

陆地车辆单目视觉/INS/GNSS集成系统快速准确初始化


初始化是多传感器集成状态估计的一项基本任务。在本文中，我们提出了一种新颖的方法来实现集成系统的快速准确的初始化。利用 GNSS 的输出，计算粗略的横摇、俯仰、偏航和重力方向。基于这些初始状态，只要观测结果可用，GNSS/INS 融合和视觉惯性里程计 (VIO) 就会同时启动。随后，通过将 GNSS/INS 融合的初始状态分配给 VIO 程序来检索粗尺度。该比例可用于获得非常接近现实世界的姿势和点。一旦 VIO 估计收敛，VIO 和 GNSS/INS 融合位姿之间的变换参数就可以通过非线性优化来估计。因此，我们可以对齐这两条轨迹并评估尺度误差。通过六次车辆测试，我们分析了所提出方法在不同光照、障碍物和运动模式下的性能，并将所提出方法的结果与最先进的算法进行了比较。结果表明，在我们的方法中，视觉惯性参数在 9 秒内收敛。我们两种方法的尺度误差大多在 10% 以内。在我们的方法中，对准误差大多在厘米级，这与现有方法相当。