基于四叉树法和PROSAC 算法改进的视觉SLAM 技术

为解决在同时定位与地图构建(simultaneous localization and mapping，SLAM)的前端进行特征点匹配时， 随机抽样一致法(random sample consensus，RANSAC)存在的迭代次数高、实时性较差、鲁棒性不稳定等问题，提出 一种基于四叉树法和渐进一致采样法(progressive sample consensus，PROSAC)算法融合改进的图像匹配算法。实现四 叉树法+PROSAC 算法的误匹配剔除算法，在EuRoC 数据集上对改进后的ORB-SLAM2 算法进行实验。结果表明： 相比于ORB-SLAM2 系统，该算法在Vicon Room 1 03 数据集上总体绝对轨迹误差平均值减小了39.28%，总体相对 位姿误差减小了35.45%，具有更高的建图精度。

Improved Visual SLAM Technology Based on Quadtree Method and PROSAC Algorithm

In order to solve the problems of RANSAC, such as high number of iterations, poor real-time performance and unstable robustness in the front end of simultaneous localization and mapping (SLAM), an improved image matching algorithm based on the fusion of quadtree method and PROSAC algorithm is proposed. The mismatching elimination algorithm of quadtree method + PROSAC algorithm is implemented, and the improved ORB-SLAM2 algorithm is tested on EuRoC data set. The results show that compared with ORB-SLAM2 system, the proposed algorithm reduces the average absolute trajectory error by 39.28% and the relative pose error by 35.45% on Vicon Room 1 03 dataset, and has higher mapping accuracy.