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We propose a lightweight method for dense online monocular depth estimation capable of reconstructing 3D meshes on computationally constrained platforms. Our main contribution is to pose the reconstruction problem as a non-local variational optimization over a time-varying Delaunay graph of the scene geometry, which allows for an efficient, keyframeless approach to depth estimation. The graph can...
We propose a method of real-time monocular camera-based localization in known environments. With the goal of controlling high-speed micro air vehicles (MAVs), we localize with respect to a mesh map of the environment that can support both pose estimation and trajectory planning. Using only limited hardware that can be carried on a MAV, we achieve accurate pose estimation at rates above 50 Hz, an order...
We present a method for Simultaneous Localization and Mapping (SLAM) using a monocular camera that is capable of reconstructing dense 3D geometry online without the aid of a graphics processing unit (GPU). Our key contribution is a multi-resolution depth estimation and spatial smoothing process that exploits the correlation between low-texture image regions and simple planar structure to adaptively...
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