Feng Zheyu, Li Jianwen, Zhang Lundong, Chen Chen
Information Engineering University, Zhengzhou 450001, China.
Sensors (Basel). 2019 May 16;19(10):2273. doi: 10.3390/s19102273.
Owing to the nonlinearity in visual-inertial state estimation, sufficiently accurate initial states, especially the spatial and temporal parameters between IMU (Inertial Measurement Unit) and camera, should be provided to avoid divergence. Moreover, these parameters are required to be calibrated online since they are likely to vary once the mechanical configuration slightly changes. Recently, direct approaches have gained popularity for their better performance than feature-based approaches in little-texture or low-illumination environments, taking advantage of tracking pixels directly. Based on these considerations, we perform a direct version of monocular VIO (Visual-inertial Odometry), and propose a novel approach to initialize the spatial-temporal parameters and estimate them with all other variables of interest (IMU pose, point inverse depth, etc.). We highlight that our approach is able to perform robust and accurate initialization and online calibration for the spatial and temporal parameters without utilizing any prior information, and also achieves high-precision estimates even when large temporal offset occurs. The performance of the proposed approach was verified through the public UAV (Unmanned Aerial Vehicle) dataset.
由于视觉惯性状态估计中的非线性特性，需要提供足够准确的初始状态，尤其是惯性测量单元（IMU）和相机之间的空间和时间参数，以避免估计结果发散。此外，这些参数需要在线校准，因为一旦机械结构稍有变化，它们就可能改变。近年来，直接方法因其在少纹理或低光照环境下比基于特征的方法具有更好的性能而受到欢迎，它直接利用像素跟踪。基于这些考虑，我们实现了单目视觉惯性里程计（VIO）的直接版本，并提出了一种新颖的方法来初始化时空参数，并与所有其他感兴趣的变量（IMU姿态、点反深度等）一起对其进行估计。我们强调，我们的方法能够在不利用任何先验信息的情况下，对空间和时间参数进行稳健且准确的初始化和在线校准，并且即使在出现较大时间偏移时也能实现高精度估计。所提方法的性能通过公开的无人机（UAV）数据集得到了验证。
