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Considering the scene's dynamics is the most effective solution to obtain anaccurate perception of unknown environments for real vSLAM applications. Mostexisting methods attempt to address the non-rigid scene assumption by combininggeometric and semantic approaches to determine dynamic elements that lackgeneralization and scene awareness. We propose a novel approach that overcomesthese limitations by using scene-depth information to improve the accuracy ofthe localization from geometric and semantic modules. In addition, we use depthinformation to determine an area of influence of dynamic objects through anObject Interaction Module that estimates the state of both non-matched andnon-segmented key points. The obtained results on TUM-RGBD dataset clearlydemonstrate that the proposed method outperforms the state-of-the-art.

D2SLAM: Semantic visual SLAM based on the Depth-related influence on object interactions for Dynamic environments