多相机与IMU融合的室外机器人定位方法研究

提高SLAM(simultaneous localization and mapping)算法的精度和鲁棒性是解决室外移动机器人自主定位问题的关键.针对单目相机在室外复杂环境下易受到遮挡、相机移动过快、图像模糊以及机器人纯旋转下算法精度和鲁棒性下降、低精度IMU(inertial measurement unit)的累...

Research of Outdoor Robot Localization Method on Fusion of Multi-camera and IMU

Improving the accuracy and robustness of SLAM algorithm is the key to solve the autonomous localization problem of outdoor mobile robot. MCSI-VINS, a method which integrates multi-camera and IMU, is proposed to solve the problems of monocular camera being vulnerable to occlusion in outdoor complex environment, camera moving too fast, image blurriness, algorithm accuracy and robustness decline under pure robot rotation, and cumulative offset of low-precision IMU. The layout of multiple cameras can make up for the case that the single eye is blocked, the rich visual information can make up for the accumulated error caused by the low-precision IMU, and the IMU fusion can solve the localization problem in the case that the camera moves too fast, the image is blurred, and the rotation is pure. It mainly introduces the data preprocessing, initialization and optimization localization module of the MCSI-VINS. This method is based on the VINS-MONO framework, but different from it, a method to build buffer based on queue data structure is proposed in the data pretreatment stage, and an initialization method based on the main camera is proposed in the front end. In the back end, the error term of other cameras is added and the time stamp is optimized. A mobile robot experiment platform is built based on ROS. Experiments verify that the proposed algorithm is more robust and accurate than the classical monocular and binocular VINS algorithms.