Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps
Fabian Bl¨ochliger, Marius Fehr, Marcin Dymczyk, Thomas Schneider and Roland Siegwart
Topomap：基于Visual SLAM地图的拓扑映射和导航
https://arxiv.org/pdf/1709.05533.pdf
Abstract—Visual robot navigation within large-scale, semistructured environments deals with various challenges such as computation intensive path planning algorithms or insufficient knowledge about traversable spaces. Moreover, many state of-the-art navigation approaches only operate locally instead of gaining a more conceptual understanding of the planning objective. This limits the complexity of tasks a robot can accomplish and makes it harder to deal with uncertainties that are present in the context of real-time robotics applications.
大规模半结构化环境中的视觉机器人导航处理各种挑战，例如计算密集型路径规划算法或关于可穿越空间的不充分知识。此外，许多最先进的导航方法仅在本地运行，而不是对规划目标进行更概念性的理解。这限制了机器人可以完成的任务的复杂性，并且使得处理实时机器人应用中存在的不确定性变得更加困难。
In this work, we present Topomap, a framework which simplifies the navigation task by providing a map to the robot which is tailored for path planning use. This novel approach transforms a sparse feature-based map from a visual Simultaneous Localization And Mapping (SLAM) system into a three-dimensional topological map. This is done in two steps. First, we extract occupancy information directly from the noisy sparse point cloud. Then, we create a set of convex free-space clusters, which are the vertices of the topological map. We show that this representation improves the efficiency of global planning, and we provide a complete derivation of our algorithm. Planning experiments on real world datasets demonstrate that we achieve similar performance as RRT* with significantly lower computation times and storage requirements. Finally, we test our algorithm on a mobile robotic platform to prove its advantages.
在这项工作中，我们介绍了Topomap，这是一个简化导航任务的框架，它为机器人提供了一个专为路径规划使用而定制的地图。这种新颖的方法将稀疏的基于特征的地图从视觉同时定位和建图（SLAM）系统转换为三维拓扑地图。这分两步完成。 首先，我们直接从嘈杂的稀疏点云中提取占用信息。然后，我们创建一组凸自由空间簇，它们是拓扑图的顶点。我们证明了这种表示提高了全局规划的效率，并且我们提供了算法的完整推导。在现实世界数据集上进行规划实验表明，我们实现了与RRT *类似的性能，同时显着降低了计算时间和存储要求。最后，我们在移动机器人平台上测试我们的算法，以证明其优势。