SafeLand: Safe Autonomous Landing in Unknown Environments with Bayesian Semantic Mapping

2026-03-18

AI Summary

This work addresses the challenge of enabling safe autonomous drone landing in unknown dynamic environments, particularly near humans and infrastructure, where existing approaches often rely on prior maps, heavy sensors, or fail to handle non-cooperative moving obstacles. The authors propose SafeLand, a lightweight vision-based system that requires only a monocular camera and a compact altimeter, operating without any prior environmental knowledge. Its key innovations include a multi-dataset semantic segmentation model (70.22% mIoU) with embedded optimization, Bayesian semantic mapping, and a temporal semantic decay mechanism to generate a reliable ground semantics map in real time. A behavior-tree-driven adaptive landing policy ensures zero missed detections of dynamic obstacles such as pedestrians. Evaluated across 200 simulations and 60 real-world trials, SafeLand achieves a 95% landing success rate, zero false negatives in human detection, and sub-second response latency, significantly outperforming current methods.

Technology Category

• Computer Vision: Vision for Robotics & Autonomous Driving
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Autonomous landing of uncrewed aerial vehicles (UAVs) in unknown, dynamic environments poses significant safety challenges, particularly near people and infrastructure, as UAVs transition to routine urban and rural operations. Existing methods often rely on prior maps, heavy sensors like LiDAR, static markers, or fail to handle non-cooperative dynamic obstacles like humans, limiting generalization and real-time performance. To address these challenges, we introduce SafeLand, a lean, vision-based system for safe autonomous landing (SAL) that requires no prior information and operates only with a camera and a lightweight height sensor. Our approach constructs an online semantic ground map via deep learning-based semantic segmentation, optimized for embedded deployment and trained on a consolidation of seven curated public aerial datasets (achieving 70.22% mIoU across 20 classes), which is further refined through Bayesian probabilistic filtering with temporal semantic decay to robustly identify metric-scale landing spots. A behavior tree then governs adaptive landing, iteratively validates the spot, and reacts in real time to dynamic obstacles by pausing, climbing, or rerouting to alternative spots, maximizing human safety. We extensively evaluate our method in 200 simulations and 60 end-to-end field tests across industrial, urban, and rural environments at altitudes up to 100m, demonstrating zero false negatives for human detection. Compared to the state of the art, SafeLand achieves sub-second response latency, substantially lower than previous methods, while maintaining a superior success rate of 95%. To facilitate further research in aerial robotics, we release SafeLand's segmentation model as a plug-and-play ROS package, available at https://github.com/markus-42/SafeLand.

Problem

• autonomous landing
• unknown environments
• dynamic obstacles
• safety
• UAV

Innovation

• Bayesian semantic mapping
• vision-based autonomous landing
• dynamic obstacle avoidance
• behavior tree
• embedded semantic segmentation