CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth
Xingxing Zuo Nathaniel Merrill Wei Li Yong Liu Marc Pollefeys Guoquan Huang Xingxing Zuo Nathaniel Merrill Wei Li Yong Liu Marc Pollefeys Guoquan Huang
In this work, we present a lightweight, tightly-coupled deep depth network and visual-inertial odometry (VIO) system, which can provide accurate state estimates and dense depth maps of the immediate surroundings. Leveraging the proposed lightweight Conditional Variational Autoencoder (CVAE) for depth inference and encoding, we provide the network with previously marginalized sparse features from V...