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Scalable Outdoors Autonomous Drone Flight With Visual-Inertial SLAM and Dense Submaps Built Without LiDAR

MCML Authors

Sotiris Papatheodorou

Sotiris Papatheodorou

* Former Member

→ Group Stefan Leutenegger

* Former Principal Investigator


Stefan Leutenegger

Stefan Leutenegger

Prof. Dr.

Principal Investigator

* Former Principal Investigator

Abstract

Autonomous navigation is needed for several robotics applications. In this paper we present an autonomous Micro Aerial Vehicle (MAV) system which purely relies on cost-effective and light-weight passive visual and inertial sensors to perform large-scale autonomous navigation in outdoor, unstructured and cluttered environments. We leverage visual-inertial simultaneous localization and mapping (VI-SLAM) for accurate MAV state estimates and couple it with a volumetric occupancy submapping system to achieve a scalable mapping framework which can be directly used for path planning. To ensure the safety of the MAV during navigation, we also propose a novel reference trajectory anchoring scheme that deforms the reference trajectory the MAV is tracking upon state updates from the VI-SLAM system in a consistent way, even upon large state updates due to loop-closures. We thoroughly validate our system in both real and simulated forest environments and at peak velocities up to 3 m/s – while not encountering a single collision or system failure. To the best of our knowledge, this is the first system which achieves this level of performance in such an unstructured environment using low-cost passive visual sensors and fully on-board computation, including VI-SLAM.

inproceedings LBP+25a

IROS 2025

IEEE/RSJ International Conference on Intelligent Robots and Systems. Hangzhou, China, Oct 19-25, 2025.
Conference logo
A Conference

Authors

S. B. Laina • S. Boche • S. Papatheodorou • D. Tzoumanikas • S. Schaefer • H. Chen • S. Leutenegger

Links

DOI GitHub

Research Areas

 B1 | Computer Vision

 B3 | Multimodal Perception

BibTeXKey: LBP+25a

#p-cremers #p-leutenegger
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