论文标题
VP-SLAM：单眼实时视觉大满贯，点，线和消失点
VP-SLAM: A Monocular Real-time Visual SLAM with Points, Lines and Vanishing Points
论文作者
论文摘要
传统的单眼视觉同时定位和映射（VSLAM）系统可以分为三类：使用特征的那些，依赖图像本身的功能以及混合模型。在基于功能的方法的情况下，新的研究已经发展为使用几何原始图（例如线和平面）的几何原始图来合并其环境中的更多信息。这是因为在许多环境中，是人为的环境，以曼哈顿世界为特征，诸如线条和飞机之类的几何图形都占据了环境中的大部分空间。这些方案的开发可能会导致引入能够优化视觉大满贯系统轨迹并有助于构建繁殖地图的算法。因此，我们提出了一个实时的单眼视觉SLAM系统，该系统结合了线条和VP提取的实时方法，以及两种策略，可以利用消失的点来估算机器人的翻译并改善其旋转的旋转。尤其是我们基于Orb-Slam 2，在ORB-SLAM 2上建立在ORB-SLAM上，以确定的策略以及范围的策略，并扩展了策略，并将其范围延长，并延伸了范围，并将其范围延长，并在范围内进行策略，并构建了vers的效率和VPS，并构建了效率和vpter，并构成了vpter的范围。第二个从已知旋转中完善翻译部分。首先，我们使用实时方法提取VP，并将其用于全局旋转优化策略。其次，我们提出了一种翻译估计方法，该方法利用最后一阶段的旋转优化来建模线性系统。最后，我们在TUM RGB-D基准上评估了系统，并证明所提出的系统实现了最先进的结果并实时运行，并且其性能仍然接近原始的Orb-Slam2系统
Traditional monocular Visual Simultaneous Localization and Mapping (vSLAM) systems can be divided into three categories: those that use features, those that rely on the image itself, and hybrid models. In the case of feature-based methods, new research has evolved to incorporate more information from their environment using geometric primitives beyond points, such as lines and planes. This is because in many environments, which are man-made environments, characterized as Manhattan world, geometric primitives such as lines and planes occupy most of the space in the environment. The exploitation of these schemes can lead to the introduction of algorithms capable of optimizing the trajectory of a Visual SLAM system and also helping to construct an exuberant map. Thus, we present a real-time monocular Visual SLAM system that incorporates real-time methods for line and VP extraction, as well as two strategies that exploit vanishing points to estimate the robot's translation and improve its rotation.Particularly, we build on ORB-SLAM2, which is considered the current state-of-the-art solution in terms of both accuracy and efficiency, and extend its formulation to handle lines and VPs to create two strategies the first optimize the rotation and the second refine the translation part from the known rotation. First, we extract VPs using a real-time method and use them for a global rotation optimization strategy. Second, we present a translation estimation method that takes advantage of last-stage rotation optimization to model a linear system. Finally, we evaluate our system on the TUM RGB-D benchmark and demonstrate that the proposed system achieves state-of-the-art results and runs in real time, and its performance remains close to the original ORB-SLAM2 system
