Autonomous Flight in Unknown GNSS-denied Environments for Disaster Examination
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Mar 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1.
3 papers.
- 2021Real-time Multi-Adaptive-Resolution-Surfel 6D LiDAR Odometry using Continuous-time Trajectory Optimizationcited by 9
- 20223D Semantic Scene Perception using Distributed Smart Edge Sensorscited by 5
- 2024LiDAR-based Registration against Georeferenced Models for Globally Consistent Allocentric Mapscited by 0
- 2024LiDAR-based Registration against Georeferenced Models for Globally Consistent Allocentric Maps3 Dec 2024
- 20223D Semantic Scene Perception using Distributed Smart Edge Sensors3 May 2022
- 2021Real-time Multi-Adaptive-Resolution-Surfel 6D LiDAR Odometry using Continuous-time Trajectory Optimization5 May 2021