# Autonomous Flight in Unknown GNSS-denied Environments for Disaster Examination

arXiv: https://arxiv.org/abs/2103.11742 · HTML: https://mlsanity.com/paper/2103.11742/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2021.

## Built on by (most cited first, 3 of 3)

- [Real-time Multi-Adaptive-Resolution-Surfel 6D LiDAR Odometry using Continuous-time Trajectory Optimization](https://mlsanity.com/paper/2105.02010/) (2021)
- [3D Semantic Scene Perception using Distributed Smart Edge Sensors](https://mlsanity.com/paper/2205.01460/) (2022)
- [LiDAR-based Registration against Georeferenced Models for Globally Consistent Allocentric Maps](https://mlsanity.com/paper/2412.02533/) (2024)

## Abstract (arXiv)

Micro aerial vehicles (MAVs) have high potential for information gathering tasks to support situation awareness in search and rescue scenarios. Manually controlling MAVs in such scenarios requires experienced pilots and is error-prone, especially in stressful situations of real emergencies. The conditions of disaster scenarios are also challenging for autonomous MAV systems. The environment is usually not known in advance and GNSS might not always be available. We present a system for autonomous MAV flights in unknown environments which does not rely on global positioning systems. The method is evaluated in multiple search and rescue scenarios and allows for safe autonomous flights, even when transitioning between indoor and outdoor areas.