# Visual Marker Search for Autonomous Drone Landing in Diverse Urban Environments

arXiv: https://arxiv.org/abs/2601.11078 · HTML: https://mlsanity.com/paper/2601.11078/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 16 Jan 2026.

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [AerialVLN: Vision-and-Language Navigation for UAVs](https://mlsanity.com/paper/2308.06735/) (2023)
- [3D-Aware Object Goal Navigation via Simultaneous Exploration and Identification](https://mlsanity.com/paper/2212.00338/) (2022)
- [Search for or Navigate to? Dual Adaptive Thinking for Object Navigation](https://mlsanity.com/paper/2208.00553/) (2022)
- [Aligning Knowledge Graph with Visual Perception for Object-goal Navigation](https://mlsanity.com/paper/2402.18892/) (2024)
- [DrIFT: Autonomous Drone Dataset with Integrated Real and Synthetic Data, Flexible Views, and Transformed Domains](https://mlsanity.com/paper/2412.04789/) (2024)
- [GARL: Genetic Algorithm-Augmented Reinforcement Learning to Detect Violations in Marker-Based Autonomous Landing Systems](https://mlsanity.com/paper/2310.07378/) (2023)
- [The Use of Multi-Scale Fiducial Markers To Aid Takeoff and Landing Navigation by Rotorcraft](https://mlsanity.com/paper/2309.08769/) (2023)
- [A Precision Drone Landing System using Visual and IR Fiducial Markers and a Multi-Payload Camera](https://mlsanity.com/paper/2403.03806/) (2024)
- [YoloTag: Vision-based Robust UAV Navigation with Fiducial Markers](https://mlsanity.com/paper/2409.02334/) (2024)
- [Towards Robust Autonomous Landing Systems: Iterative Solutions and Key Lessons Learned](https://mlsanity.com/paper/2505.12176/) (2025)

## Abstract (arXiv)

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.