The odometry is an important part of intelligent mobile robots to achieve positioning and navigation functions. At present, the mainstream visual odometry locates only through the visual information obtained by camera sensors. Therefore, in the case of insufficient light, texture missing and camera jitter, the visual odometry is difficult to locate accurately. To solve the problem, we propose a binocular MSCKF-based visual inertial odometry system using Lucas-Kanade (LK) optical flow. Firstly, the Inertial Measurement Unit (IMU) is introduced to overcome the above problems. Moreover, LK optical flow algorithm is utilized to process the visual information obtained by the binocular camera, and MSCKF algorithm is employed to realize the fusion of visual information and inertial information, which improves the accuracy and efficiency of the visual inertial odometry system positioning. Finally, the proposed method is simulated on the European Robotics Challenge (EuRoc) dataset by Robot Operating System (ROS), and compared with two other advanced visual inertial odometry systems, ROVIO and MSCKF-mono. A large number of simulations verify that the proposed method can achieve accurate pose estimation, which is superior to the two existing advanced visual inertial odometry systems.

里程表是智能移动机器人实现定位和导航功能的重要组成部分。目前，主流的视觉里程表仅通过相机传感器获得的视觉信息进行定位。因此，在光线不足，纹理缺失和相机抖动的情况下，视觉测距法很难准确定位。为了解决该问题，我们提出了一种使用卢卡斯-卡纳德（LK）光流的基于双目MSCKF的视觉惯性里程计系统。首先，引入惯性测量单元（IMU）来克服上述问题。此外，利用LK光流算法处理双目相机获得的视觉信息，采用MSCKF算法实现视觉信息与惯性信息的融合，从而提高了视觉惯性里程系统定位的准确性和效率。最后，通过机器人操作系统（ROS）在欧洲机器人挑战赛（EuRoc）数据集上对提出的方法进行了仿真，并将其与其他两个高级视觉惯性里程计系统ROVIO和MSCKF-mono进行了比较。大量的仿真验证了所提出的方法可以实现准确的姿态估计，这优于两个现有的高级视觉惯性里程测量系统。