IT-SVO: Improved Semi-Direct Monocular Visual Odometry Combined with JS Divergence in Restricted Mobile Devices.

ABSTRACT: Simultaneous localization and mapping (SLAM) has a wide range for applications in mobile robotics. Lightweight and inexpensive vision sensors have been widely used for localization in GPS-denied or weak GPS environments. Mobile robots not only estimate their pose, but also correct their position according to the environment, so a proper mathematical model is required to obtain the state of robots in their circumstances. Usually, filter-based SLAM/VO regards the model as a Gaussian distribution in the mapping thread, which deals with the complicated relationship between mean and covariance. The covariance in SLAM or VO represents the uncertainty of map points. Therefore, the methods, such as probability theory and information theory play a significant role in estimating the uncertainty. In

SUBMITTER: Liu C 

PROVIDER: S-EPMC7998773 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature