  In this paper, we present an omnidirectional localization and dense mapping
system for a wide-baseline multiview stereo setup with ultra-wide field-of-view
(FOV) fisheye cameras, which has a 360 degrees coverage of stereo observations
of the environment. For more practical and accurate reconstruction, we first
introduce improved and light-weighted deep neural networks for the
omnidirectional depth estimation, which are faster and more accurate than the
existing networks. Second, we integrate our omnidirectional depth estimates
into the visual odometry (VO) and add a loop closing module for global
consistency. Using the estimated depth map, we reproject keypoints onto each
other view, which leads to a better and more efficient feature matching
process. Finally, we fuse the omnidirectional depth maps and the estimated rig
poses into the truncated signed distance function (TSDF) volume to acquire a 3D
map. We evaluate our method on synthetic datasets with ground-truth and
real-world sequences of challenging environments, and the extensive experiments
show that the proposed system generates excellent reconstruction results in
both synthetic and real-world environments.
