泡泡一分鐘：Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps

Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps

Fabian Bl¨ochliger, Marius Fehr, Marcin Dymczyk, Thomas Schneider and Roland Siegwart

Topomap：基於Visual SLAM地圖的拓撲映射和導航

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.

大規模半結構化環境中的視覺機器人導航處理各類挑戰，例如計算密集型路徑規劃算法或關於可穿越空間的不充分知識。此外，許多最早進的導航方法僅在本地運行，而不是對規劃目標進行更概念性的理解。這限制了機器人能夠完成的任務的複雜性，而且使得處理實時機器人應用中存在的不肯定性變得更加困難。

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.

在這項工做中，咱們介紹了Topomap，這是一個簡化導航任務的框架，它爲機器人提供了一個專爲路徑規劃使用而定製的地圖。這種新穎的方法將稀疏的基於特徵的地圖從視覺同時定位和建圖（SLAM）系統轉換爲三維拓撲地圖。這分兩步完成。 首先，咱們直接從嘈雜的稀疏點雲中提取佔用信息。而後，咱們建立一組凸自由空間簇，它們是拓撲圖的頂點。咱們證實了這種表示提升了全局規劃的效率，而且咱們提供了算法的完整推導。在現實世界數據集上進行規劃實驗代表，咱們實現了與RRT *相似的性能，同時顯着下降了計算時間和存儲要求。最後，咱們在移動機器人平臺上測試咱們的算法，以證實其優點。