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.

翻译：考虑场景动态性是真实视觉SLAM应用中获取未知环境准确感知的最有效方法。现有方法大多通过结合几何与语义方法来确定动态元素，以解决非刚性场景假设问题，但这些方法缺乏泛化能力和场景感知能力。我们提出了一种新型方法，通过利用场景深度信息来提升几何模块与语义模块的定位精度，从而克服上述局限性。此外，我们通过物体交互模块利用深度信息确定动态物体的影响区域，该模块能够估计未匹配和未分割关键点的状态。在TUM-RGBD数据集上获得的实验结果明确表明，所提方法优于现有最先进技术。