论文标题
D2SLAM：基于对动态环境的对象相互作用的深度相关影响的语义视觉猛击
D2SLAM: Semantic visual SLAM based on the Depth-related influence on object interactions for Dynamic environments
论文作者
论文摘要
考虑到场景的动态是最有效的解决方案，以获得对实际VSLAM应用的未知环境的准确感知。大多数现有方法试图通过结合几何和语义方法来确定缺乏概括和场景意识的动态元素来解决非刚性场景假设。我们提出了一种新颖的方法，该方法通过使用场景深度信息来克服这些局限性，以提高从几何和语义模块中定位的准确性。此外，我们使用深度信息通过对象交互模块来确定动态对象的影响区域，该模块估计了非匹配和非分段关键点的状态。在TUM-RGBD数据集上获得的结果清楚地表明，所提出的方法的表现优于最先进的方法。
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.
