基于视觉惯性融合的半直接单目视觉里程计

针对半直接单目视觉里程计缺乏尺度信息并且在快速运动中鲁棒性较差的缺点，设计了一种融合惯性测量信息的半直接单目视觉里程计，通过IMU（惯性测量单元）信息弥补视觉里程计的缺陷，有效提高跟踪精度与系统鲁棒性．本文联合惯性测量信息与视觉信息进行初始化，较准确地恢复了环境尺度信息．为提高运动跟踪的鲁棒性，提出一种IMU加权的运动先验模型．通过预积分获取IMU的状态估计，根据IMU先验误差调整权重系数，使用IMU先验信息的加权值为前端提供精确的初值．后端构建了紧耦合的图优化模型，融合惯性、视觉以及3维地图点信息进行联合优化，同时在滑动窗口中使用强共视关系作为约束，在消除局部累积误差的同时提高优化效率与优化精度．实验结果表明，本文的先验模型优于匀速运动模型与IMU先验模型，单帧先验误差小于1 cm．后端优化方法改进后，计算效率提高为原来的1.52倍，同时轨迹精度与优化稳定性也得到提高．在EuRoC数据集上进行测试，定位效果优于OKVIS算法，轨迹均方根误差减小为原视觉里程计的1/3．

Semi-Direct Monocular Visual Odometry Based on Visual-Inertial Fusion

Considering the disadvantages of semi-direct monocular visual odometry which lacks scale factor and shows poor robustness in fast motion, a semi-direct monocular visual odometry based on inertial fusion is designed. By using IMU (inertial measurement unit) information to make up for the deficiencies of visual odometry, the tracking accuracy and the system robustness can be effectively improved. Through the joint initialization of visual information and inertial measurement, the environmental scale can be accurately recovered. In order to improve the robustness of motion tracking, an IMU-weighted prior model is proposed. IMU state estimation is obtained by preintegration, the weight coefficient is adjusted according to the IMU prior error, and then the weighted value of IMU state is used to provide accurate initial estimation for the front-end. A tightly coupled graph optimization model is constructed in the back-end, which combines inertia, vision and 3D map point for joint optimization. Using common-view relationships as constraints in sliding window, it can improve the optimization efficiency and accuracy while eliminating the local cumulative error. The experimental results show that the prior model is better than both the uniform motion model and the IMU prior model, and the single frame prior error is less than 1 cm. By improving the back-end method, the calculation efficiency is increased by 0.52 times, and both the trajectory accuracy and the optimization stability are improved at the same time. Experimental results on the public dataset EuRoC demonstrate that the proposed algorithm outperforms the open keyframe-based visual inertial SLAM (OKVIS) algorithm, and the root mean square error of trajectory is reduced to 1/3 compared with the original visual odometry.