Traditional light detection and ranging (lidar) loop-closure detection algorithms are greatly interfered with by dynamic obstacles, and key-frame search and feature matching take a long time. In response, this paper proposed a less time-consuming SR-Context lidar loop-closure detection algorithm with stronger robustness based on the multiple-features random sample consensus (MF-RANSAC) algorithm and an improved ScanContext algorithm. Firstly, the region growing algorithm was used to cluster the point clouds that had undergone fan-shaped rasterization. Then, an MF-RANSAC algorithm was proposed to eliminate dynamic targets quickly. This algorithm was based on multi-point selection in a dynamic region and query of corresponding points with multiple attributes rather than target recognition and tracking. Finally, the ScanContext algorithm was improved by simplifying the feature matching calculation and deleting the loop-closure historical matching frames. Loop-closure detection of the point clouds of the current frame after elimination of dynamic targets was thus achieved. Tests were carried out on a KITTI dataset and a real vehicle dataset. The experimental results show that the proposed method delivers quick and accurate loop-closure detection in dynamic urban environments and thereby improves lidar mapping accuracy. The average time it takes is only 40% of that of the ScanContext algorithm.