Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps

TL;DR

Topomap introduces a novel topological mapping framework that converts sparse visual SLAM maps into 3D topological maps, enhancing global planning efficiency and reducing computational costs in robot navigation.

Contribution

It presents a new method to transform sparse SLAM maps into topological maps for improved path planning in large-scale environments.

Findings

Achieves similar planning performance as RRT*

Reduces computation time and storage requirements

Demonstrates effectiveness on real-world robotic platform

Abstract

Visual robot navigation within large-scale, semi-structured 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. 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.