As a research hotspot in the field of robotics, Simultaneous localization and mapping (SLAM) has made great progress in recent years, but few SLAM algorithms take dynamic or movable targets in the scene into account. In this paper, a robust new RGB-D SLAM method with dynamic area detection towards dynamic environments named GMSK-SLAM is proposed. Most of the existing related papers use the method of directly eliminating the whole dynamic targets. Although rejecting dynamic objects can increase the accuracy of robot positioning to a certain extent, this type of algorithm will result in the reduction of the number of available feature points in the image. The lack of sufficient feature points will seriously affect the subsequent precision of positioning and mapping for feature-based SLAM. The proposed GMSK-SLAM method innovatively combines Grid-based Motion Statistics (GMS) feature points matching method with K-means cluster algorithm to distinguish dynamic areas from the images and retain static information from dynamic environments, which can effectively increase the number of reliable feature points and keep more environment features. This method can achieve a highly improvements on localization accuracy in dynamic environments. Finally, sufficient experiments were conducted on the public TUM RGB-D dataset. Compared with ORB-SLAM2 and the RGB-D SLAM, our system, respectively, got 97.3% and 90.2% improvements in dynamic environments localization evaluated by root-mean-square error. The empirical results show that the proposed algorithm can eliminate the influence of the dynamic objects effectively and achieve a comparable or better performance than state-of-the-art methods.

作为机器人领域的研究热点，同步定位与建图（SLAM）近年来取得了很大进展，但很少有SLAM算法考虑到场景中的动态或可移动目标。在本文中，提出了一种鲁棒的新 RGB-D SLAM 方法，该方法具有针对动态环境的动态区域检测，名为 GMSK-SLAM。现有的相关论文大多采用直接剔除整个动态目标的方法。虽然拒绝动态物体可以在一定程度上提高机器人定位的精度，但这种算法会导致图像中可用特征点的数量减少。缺乏足够的特征点会严重影响后续基于特征的SLAM定位和建图的精度。提出的 GMSK-SLAM 方法创新地将基于网格的运动统计 (GMS) 特征点匹配方法与 K-means 聚类算法相结合，从图像中区分动态区域，从动态环境中保留静态信息，可有效增加可靠特征的数量。点并保留更多环境特征。该方法可以极大地提高动态环境中的定位精度。最后，在公共 TUM RGB-D 数据集上进行了足够的实验。与 ORB-SLAM2 和 RGB-D SLAM 相比，我们的系统在通过均方根误差评估的动态环境定位方面分别获得了 97.3% 和 90.2% 的改进。