VIO-SLAM指的是移动机器人把相机和IMU作为外部传感器, 在进行自身定位的同时构建外部地图。从前端视觉惯导里程计、后端优化、回环检测和建图等模块对VIO-SLAM定位系统进行综述。对于视觉惯导里程计,主要讲述特征点法和光流法的异同, 以及IMU预积分的过程; 后端优化主要指出如何处理VIO-SLAM过程中的噪声以及IMU中陀螺仪和加速度的数据误差; 回环检测主要阐述如何解决位置估计随时间漂移的问题; 而建图部分则依据移动机器人的用途指出几种主要的建图方法和建图策略。最后对VIO-SLAM的发展趋势做出展望。

VIO-SLAM (Visual Inertial Odometer Simultaneous Localization and Mapping)means that mobile robots use cameras and IMUs as external sensors to build external maps while positioning themselves.This paper reviews the VIO-SLAM positioning system from such modules as the front-end visual inertial navigation odometer, back-end optimization, loop detection and the mapping.As to the visual inertial navigation odometer, the similarities and differences between the feature point method and the optical flow method, and the process of IMU pre-integration are stated.Back-end optimization mainly points out how to deal with the noise in VIO-SLAM process and the bias of the gyroscope and acceleration in IMU.Loop detection mainly expounds how to solve the problem of position estimation drifting with time.In the mapping part, several main mapping methods and strategies are given according to the purpose of mobile robots.Finally, the development trend and prospect of VIO-SLAM are given.