  Considering the scene's dynamics is the most effective solution to obtain an
accurate perception of unknown environments for real vSLAM applications. Most
existing methods attempt to address the non-rigid scene assumption by combining
geometric and semantic approaches to determine dynamic elements that lack
generalization and scene awareness. We propose a novel approach that overcomes
these limitations by using scene-depth information to improve the accuracy of
the localization from geometric and semantic modules. In addition, we use depth
information to determine an area of influence of dynamic objects through an
Object Interaction Module that estimates the state of both non-matched and
non-segmented key points. The obtained results on TUM-RGBD dataset clearly
demonstrate that the proposed method outperforms the state-of-the-art.
