基于点线特征视觉惯性融合的机器人SLAM算法

Robot SLAM algorithm based on visual inertia fusion of point-line features

针对弱纹理环境下单目视觉SLAM系统只依靠提取点特征鲁棒性较差的问题,提出一种点线特征视觉与惯导融合的机器人SLAM算法。首先,采用自适应加权提取点线特征并使用普吕克坐标法表示线段,减小计算量同时较好克服线特征提取时线段割裂的不足;其次,采用四叉树法实现点线特征提取均匀化解决特征堆积问题,同时消除点线特征误匹配,再利用视觉点线信息与IMU紧耦合优化机制提高机器人SLAM算法精确度。最后,将该算法在EuRoC数据集和弱纹理环境中进行实验,结果表明,改进后线特征提取相较于传统线特征提取鲁棒性提高了12.94%,相较于原生算法ORB-SLAM3,改进后特征匹配时间节约了19.2%,大型弱纹理环境中绝对定位精度提高了55.6%,所提算法在弱纹理环境中定位效果具有较强的鲁棒性和精确性。

Aiming at the poor robustness of monocular vision SLAM system in weak texture environment only by extracting point features, a robot SLAM algorithm based on point-line feature vision and inertial navigation is proposed. Firstly, adaptive weighted point-line features are extracted, and Plücker coordinate method is used to represent line segments, which reduces the amount of calculation and overcomes the shortage of line segment fragmentation in line feature extraction. Secondly, the quadtree method is used to realize the homogenization of point-line feature extraction to solve the problem of feature accumulation, at the same time eliminate the mismatching of point-line features, and then the tight coupling optimization mechanism of visual point-line information and IMU is used to improve the accuracy of robot SLAM algorithm. Finally, the proposed algorithm is tested on Eu RoC data set and weak texture environment. The results show that the robustness of the improved line feature extraction is improved by 12.94% compared with the traditional line feature extraction. Compared with the native algorithm ORB-SLAM3, the improved feature matching time is saved by 19.2%, and the absolute positioning accuracy in large weak texture environment is improved by 55.6%. The proposed algorithm has strong robustness and accuracy in weak texture environment.