Ma Shujun, Bai Xinhui, Wang Yinglei, Fang Rui
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China.
Sensors (Basel). 2019 Aug 29;19(17):3747. doi: 10.3390/s19173747.
The fusion of visual and inertial odometry has matured greatly due to the complementarity of the two sensors. However, the use of high-quality sensors and powerful processors in some applications is difficult due to size and cost limitations, and there are also many challenges in terms of robustness of the algorithm and computational efficiency. In this work, we present VIO-Stereo, a stereo visual-inertial odometry (VIO), which jointly combines the measurements of the stereo cameras and an inexpensive inertial measurement unit (IMU). We use nonlinear optimization to integrate visual measurements with IMU readings in VIO tightly. To decrease the cost of computation, we use the FAST feature detector to improve its efficiency and track features by the KLT sparse optical flow algorithm. We also incorporate accelerometer bias into the measurement model and optimize it together with other variables. Additionally, we perform circular matching between the previous and current stereo image pairs in order to remove outliers in the stereo matching and feature tracking steps, thus reducing the mismatch of feature points and improving the robustness and accuracy of the system. Finally, this work contributes to the experimental comparison of monocular visual-inertial odometry and stereo visual-inertial odometry by evaluating our method using the public EuRoC dataset. Experimental results demonstrate that our method exhibits competitive performance with the most advanced techniques.
由于视觉和惯性里程计这两种传感器具有互补性，它们的融合技术已经相当成熟。然而，在某些应用中，由于尺寸和成本的限制，使用高质量的传感器和强大的处理器存在困难，并且在算法的鲁棒性和计算效率方面也存在许多挑战。在这项工作中，我们提出了VIO-Stereo，一种立体视觉惯性里程计（VIO），它将立体相机和廉价的惯性测量单元（IMU）的测量结果结合在一起。我们使用非线性优化将视觉测量与VIO中的IMU读数紧密集成。为了降低计算成本，我们使用FAST特征检测器来提高效率，并通过KLT稀疏光流算法跟踪特征。我们还将加速度计偏差纳入测量模型，并与其他变量一起进行优化。此外，我们在前后立体图像对之间进行循环匹配，以消除立体匹配和特征跟踪步骤中的异常值，从而减少特征点的不匹配，提高系统的鲁棒性和准确性。最后，通过使用公开的EuRoC数据集评估我们的方法，这项工作有助于单目视觉惯性里程计和立体视觉惯性里程计的实验比较。实验结果表明，我们的方法与最先进的技术相比具有竞争力。
