基于IMU/视觉融合的导航定位算法研究

针对基于视觉传感器的移动机器人在快速运动或发生旋转时出现图像模糊和特征丢失,以至无法进行特征匹配,从而导致系统定位和建图的准确度及精确度下降问题,该文提出了一种以深度相机(RGB_D)融合惯性测量单元(IMU)的方案。采用ORB SLAM2算法进行位姿估计,同时将IMU信息作为约束弥补相机数据的缺失。两种传感器的测量数据采用基于扩展卡尔曼滤波的松耦合方式进行非线性优化,通过数据采集实验表明,该方法能有效提高机器人的定位精度和系统建图效果。

Research on Navigation and Localization Algorithm Based on IMU/Vision Fusion

Aiming at the problem that the image blurring and feature loss during fast motion or rotation of the mobile robots with visual sensors occur so that the feature matching fails resulting in the decrease of accuracy and precision of system localiztion and mapping, a schemefusing the depth camera(RGB_D) with inertial measurement unit(IMU) is proposed in this paper, in which the ORB SLAM2 algorithm is used to estimate the pose, and the IMU information is used as a constraint to compensate for the lack of camera data. The measurement data of the two sensors are nonlinearly optimized by the loose coupling method based on extended Kalman filter. The experimental results of data set show that the method can effectively improve the positioning accuracy and system mapping effect of the robot.