Abstract--We present a visual-inertial depth estimation pipeline that integrates monocular depth estimation and visualinertial odometry to produce dense depth estimates with metric scale. Here, with GA+SML, objects are aligned more accurately, the center desk leg is straightened, and the top of the desk is pulled forward. Works that use inertial data to inform metric scale typically Depth perception is fundamental to visual navigation, where perform depth completion given a set of known sparse metric correctly estimating distances can help plan motion and avoid depth points and tend to be self-supervised in nature due to a obstacles. Accurate depth estimation can also aid scene reconstruction, lack of visual-inertial datasets [6], [7]. We seek to bridge these mapping, and object manipulation. Some applications approaches by leveraging monocular depth estimation models of estimated depth benefit when it is metrically trained on diverse datasets and recovering metric scale for accurate--when every depth value is provided in absolute individual depth estimates. Our approach performs least-squares fitting of monocular Algorithms for dense depth estimation can be broadly depth estimates against sparse metric depth, followed by grouped into several categories. Stereo-based approaches rely learned local per-pixel adjustment. Structurefrom-motion and dense (local) depth alignment successfully rectifies metric (SfM) tries to estimate scene geometry from scale, with dense alignment consistently outperforming a a sequence of images taken by a moving camera, but it is purely global alignment baseline.