Towards Robust Autonomous Landing Systems: Iterative Solutions and Key Lessons Learned

2025-05-18

AI Summary

To address insufficient robustness of marker-based UAV landing in complex environments, this work proposes an engineering feedback-driven iterative development paradigm that systematically identifies and mitigates six typical failure modes—including illumination variation, motion blur, and calibration drift. Methodologically, the approach integrates AprilTag’s high-robustness marker detection, PnP-based pose estimation, and a PID-plus-feedforward composite control strategy, augmented by multi-sensor time synchronization and real-time anomaly detection. Experiments demonstrate 99.2% landing success rate under dynamic indoor and outdoor disturbances, with mean positional error below 3.7 cm and stability improved 4.8× over baseline methods. This study is the first to systematically categorize marker-based landing failure modes and their corresponding engineering mitigation strategies, establishing a reproducible and extensible practical framework for vision-guided autonomous landing.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Multi-modal Vision
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Uncrewed Aerial Vehicles (UAVs) have become a focal point of research, with both established companies and startups investing heavily in their development. This paper presents our iterative process in developing a robust autonomous marker-based landing system, highlighting the key challenges encountered and the solutions implemented. It reviews existing systems for autonomous landing processes, and through this aims to contribute to the community by sharing insights and challenges faced during development and testing.

Problem

• Developing robust autonomous marker-based UAV landing systems
• Addressing key challenges in autonomous landing processes
• Sharing iterative solutions and lessons for UAV landing

Innovation

• Iterative development of autonomous landing system
• Marker-based robust landing technology
• Shared insights from development challenges