Robust Visual Odometry Leveraging Mixture of Manhattan Frames in Indoor Environments
We propose a robust RGB-Depth (RGB-D) Visual Odometry (VO) system to improve the localization performance of indoor scenes by using geometric features, including point and line features.Previous VO/Simultaneous Localization and sunscreen Mapping (SLAM) algorithms estimate the low-drift camera poses with the Manhattan World (MW)/Atlanta World (AW) a