动态环境下改进ICP算法的RGB-D SLAM研究

针对传统的ICP(Iterative Closest Points)算法,无法满足室内动态环境下SLAM(Simultaneous Localization and Mapping)算法的准确性要求,提出了一种融合特征点结构相似性判断的ICP改进算法;通过在特征点集中引入三角形结构约束,实现两组点集中的动态匹配点与误匹配点的剔除,进而提高ORB特征点匹配的准确性;与传统的SLAM算法相比,改进后的算法对相机位姿的估计更加准确;通过在Linux系统下的仿真实验,结合特征点三角几何约束的ICP算法能够有效解决动态对象对相机位姿估计的影响,提高RGB-D SLAM在动态场景下的定位精度。

Improvement of the ICP Algorithm of RGB-D SLAM in Dynamic Environment

In view of the traditional ICP (Iterative Closest Points) algorithm which cannot satisfy the requirement of SLAM (Simultaneous Localization and Mapping) algorithm accuracy in dynamic environment,an improved ICP algorithm based on the fusion of feature point structure similarity judgment is proposed;By introducing triangular structure constraints in the feature points set, the dynamic matching points and false matching points in the two groups of point sets were eliminated, and the accuracy of ORB feature points matching was improved. Compared with traditional SLAM algorithm, the estimation of camera pose is more accurate. Through the simulation experiment in Linux system, the ICP algorithm combined with geometric constraints of feature points can effectively solve the impact of dynamic objects on camera pose estimation, and improve the positioning accuracy of RGB-D SLAM in dynamic scene.