  Visually impaired people usually find it hard to travel independently in many
public places such as airports and shopping malls due to the problems of
obstacle avoidance and guidance to the desired location. Therefore, in the
highly dynamic indoor environment, how to improve indoor navigation robot
localization and navigation accuracy so that they guide the visually impaired
well becomes a problem. One way is to use visual SLAM. However, typical visual
SLAM either assumes a static environment, which may lead to less accurate
results in dynamic environments or assumes that the targets are all dynamic and
removes all the feature points above, sacrificing computational speed to a
large extent with the available computational power. This paper seeks to
explore marginal localization and navigation systems for indoor navigation
robotics. The proposed system is designed to improve localization and
navigation accuracy in highly dynamic environments by identifying and tracking
potentially moving objects and using vector field histograms for local path
planning and obstacle avoidance. The system has been tested on a public indoor
RGB-D dataset, and the results show that the new system improves accuracy and
robustness while reducing computation time in highly dynamic indoor scenes.
