论文标题
CFP-SLAM：基于动态环境中的粗到最新概率的实时视觉大满贯
CFP-SLAM: A Real-time Visual SLAM Based on Coarse-to-Fine Probability in Dynamic Environments
论文作者
论文摘要
由于违反SLAM算法的静态环境假设，环境中的动态因素将导致摄像机定位精度下降。最近，一些相关的作品通常使用语义约束和几何约束的组合来处理动态对象，但是仍然可以提出问题，例如实时性能差，易于将人视为刚性的身体以及在低动态场景中的性能差。在本文中，提出了一种基于对象检测和称为CFP-SLAM的粗到细静态概率的动态视觉大满贯算法。该算法结合了语义约束和几何约束，以计算对象，关键点和地图点的静态概率，并将其作为权重以参与相机姿势估计。广泛的评估表明，与最新的动态大满贯方法相比，我们的方法几乎可以在高动态和低动态方案中获得最佳的结果，并且显示出很高的实时能力。
The dynamic factors in the environment will lead to the decline of camera localization accuracy due to the violation of the static environment assumption of SLAM algorithm. Recently, some related works generally use the combination of semantic constraints and geometric constraints to deal with dynamic objects, but problems can still be raised, such as poor real-time performance, easy to treat people as rigid bodies, and poor performance in low dynamic scenes. In this paper, a dynamic scene-oriented visual SLAM algorithm based on object detection and coarse-to-fine static probability named CFP-SLAM is proposed. The algorithm combines semantic constraints and geometric constraints to calculate the static probability of objects, keypoints and map points, and takes them as weights to participate in camera pose estimation. Extensive evaluations show that our approach can achieve almost the best results in high dynamic and low dynamic scenarios compared to the state-of-the-art dynamic SLAM methods, and shows quite high real-time ability.
