D2SLAM: Semantic visual SLAM based on the Depth-related influence on object interactions for Dynamic environments
This addresses the challenge of robust SLAM in dynamic scenes for robotics and AR/VR applications, but appears incremental as it builds on existing geometric and semantic methods.
The paper tackled the problem of accurate visual SLAM in dynamic environments by using depth information to improve localization and model object interactions, achieving state-of-the-art performance on the TUM-RGBD dataset.
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.