Topology-Guided Path Planning for Reliable Visual Navigation of MAVs

TL;DR

This paper introduces a topology-guided path planning method for MAVs that improves perception-aware navigation efficiency by leveraging topological environment information, outperforming sampling-based methods in accuracy and computational speed.

Contribution

It proposes a novel topology-based perception-aware path planner that efficiently selects paths with rich visual landmarks for MAV navigation.

Findings

Enhanced visual navigation accuracy in simulations and real-world tests.

Reduced computational time compared to sampling-based perception-aware planners.

Guarantees stable perception-aware paths for MAVs.

Abstract

Visual navigation has been widely used for state estimation of micro aerial vehicles (MAVs). For stable visual navigation, MAVs should generate perception-aware paths which guarantee enough visible landmarks. Many previous works on perception-aware path planning focused on sampling-based planners. However, they may suffer from sample inefficiency, which leads to computational burden for finding a global optimal path. To address this issue, we suggest a perception-aware path planner which utilizes topological information of environments. Since the topological class of a path and visible landmarks during traveling the path are closely related, the proposed algorithm checks distinctive topological classes to choose the class with abundant visual information. Topological graph is extracted from the generalized Voronoi diagram of the environment and initial paths with different topological classes are found. To evaluate the perception quality of the classes, we divide the initial path into discrete segments where the points in each segment share similar visual information. The optimal class with high perception quality is selected, and a graph-based planner is utilized to generate path within the class. With simulations and real-world experiments, we confirmed that the proposed method could guarantee accurate visual navigation compared with the perception-agnostic method while showing improved computational efficiency than the sampling-based perception-aware planner.