Visual Marker Search for Autonomous Drone Landing in Diverse Urban Environments

TL;DR

This paper evaluates marker-based drone landing methods in diverse urban environments using simulation, analyzing how different exploration strategies and scene complexities impact success and robustness.

Contribution

It introduces a simulation-based evaluation suite for autonomous drone landing in varied urban settings, comparing heuristic and reinforcement learning approaches.

Findings

Reinforcement learning agent improves landing success in complex scenes.

Scene complexity significantly affects landing robustness and efficiency.

Evaluation highlights the importance of diverse testing conditions for reliable drone navigation.

Abstract

Marker-based landing is widely used in drone delivery and return-to-base systems for its simplicity and reliability. However, most approaches assume idealized landing site visibility and sensor performance, limiting robustness in complex urban settings. We present a simulation-based evaluation suite on the AirSim platform with systematically varied urban layouts, lighting, and weather to replicate realistic operational diversity. Using onboard camera sensors (RGB for marker detection and depth for obstacle avoidance), we benchmark two heuristic coverage patterns and a reinforcement learning-based agent, analyzing how exploration strategy and scene complexity affect success rate, path efficiency, and robustness. Results underscore the need to evaluate marker-based autonomous landing under diverse, sensor-relevant conditions to guide the development of reliable aerial navigation systems.