This paper introduces a novel globally consistent dense RGB-D SLAM system designed to address the challenges of online dense reconstruction under fast camera motion. To achieve robust pose tracking and globally consistent map building, we present a random optimization pose estimation framework that integrates both geometric and photometric information. Furthermore, we propose a voxel revisitation-based loop closure detection scheme that relies solely on depth maps. Innovatively, we introduce a local curvature feature, to address loop closure detection when photometric information is unavailable, efficiently extracting 3D geometric information directly from 2D images. In addition, we leverage voxel revisitation observations to accelerate the search for potential loop closures. This is achieved by maintaining a dynamic active map and detecting voxel outflow or revisits, thereby enhancing loop closure detection efficiency. This method is the first voxel revisitation-based loop closure detection scheme that relies entirely on depth information. Combined with a random optimization pose estimation framework utilizing both geometric and photometric features, it adapts well to fast camera motion, enabling accurate and globally consistent dense map construction. Extensive evaluations on public datasets demonstrate the effectiveness of our method under fast camera motion and highlight its significant advantages over existing methods.