RSV-SLAM：面向室内动态环境的实时语义视觉SLAM
RSV-SLAM: Toward Real-Time Semantic Visual SLAM in Indoor Dynamic Environments
AI总结：
针对动态环境下视觉SLAM性能下降的问题，提出融合深度学习语义信息、扩展卡尔曼滤波与生成网络的RSV-SLAM方法，在TUM数据集上定位误差具竞争力且可近实时运行。
摘要：
同时定位与建图（Simultaneous Localization and Mapping, SLAM）在包括社交机器人在内的众多机器人领域发挥着重要作用。现有多数视觉SLAM方法都基于静态世界假设，在动态环境中性能会大幅下降。本研究提出了一种专门针对动态环境设计的实时语义RGBD SLAM方法。我们提出的系统能够有效检测运动物体并维护静态地图，以确保鲁棒的相机跟踪。该方法的核心创新在于将基于深度学习的语义信息融入SLAM系统，以减轻动态物体带来的影响。此外，我们通过集成扩展卡尔曼滤波（Extended Kalman filter）优化语义分割流程，用于识别可能处于暂时静止状态的动态物体。我们还实现了一个生成网络，用于填补输入图像中属于动态物体的缺失区域。这一高度模块化的框架已在ROS平台上实现，在GTX1080显卡上可达到约22帧每秒（fps）的运行速度。在TUM数据集的动态序列上对所开发的流程进行基准测试的结果表明，该方法与当前最优方法相比，定位误差具有竞争力，同时能够实现近实时运行。其源代码已公开。
英文摘要：
Simultaneous Localization and Mapping (SLAM) plays an important role in many robotics fields, including social robots. Many of the available visual SLAM methods are based on the assumption of a static world and struggle in dynamic environments. In the current study, we introduce a real-time semantic RGBD SLAM approach designed specifically for dynamic environments. Our proposed system can effectively detect moving objects and maintain a static map to ensure robust camera tracking. The key innovation of our approach is the incorporation of deep learning-based semantic information into SLAM systems to mitigate the impact of dynamic objects. Additionally, we enhance the semantic segmentation process by integrating an Extended Kalman filter to identify dynamic objects that may be temporarily idle. We have also implemented a generative network to fill in the missing regions of input images belonging to dynamic objects. This highly modular framework has been implemented on the ROS platform and can achieve around 22 fps on a GTX1080. Benchmarking the developed pipeline on dynamic sequences from the TUM dataset suggests that the proposed approach delivers competitive localization error in comparison with the state-of-the-art methods, all while operating in near real-time. The source code is publicly available.
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