Taking into account the dynamics of the scene is the most effective solution to obtain an accurate perception of unknown environments within the framework of a real autonomous robotic application. Many works have attempted to address the non-rigid scene assumption by taking advantage of deep learning advancements. Most new methods combine geometric and semantic approaches to determine dynamic elements that lack generalization and scene awareness. We propose a novel approach that overcomes the limitations of these methods by using scene depth information that refines the accuracy of estimates from geometric and semantic modules. In addition, the depth information is used to determine an area of influence of dynamic objects through our Objects Interaction module that estimates the state of both non-matched keypoints and out of segmented region keypoints. The obtained results demonstrate the efficacy of the proposed method in providing accurate localization and mapping in dynamic environments.

翻译：考虑到场景的动态,这是在真正的自主机器人应用框架内准确认识未知环境的最有效解决办法。许多工作都试图利用深层学习的进步来应对非硬性场景假设。大多数新方法结合了几何和语义方法,以确定缺乏一般化和对场景认识的动态要素。我们建议采用一种新颖的方法,通过利用场景深度信息来克服这些方法的局限性,从而改进从几何和语义模块得出的估计数的准确性。此外,还利用深度信息,通过我们的物体互动模块来确定动态物体的影响力范围,该模块估计了非匹配关键点和片段区域关键点之外的关键点的状况。获得的结果表明,拟议方法在动态环境中提供准确的本地化和绘图方面的效力。