Indoor UAV navigation using event cameras and intermediate frame reconstruction
Indoor UAV navigation faces significant challenges due to GPS signal absence and limitations of conventional visual-inertial systems under challenging lighting and motion conditions. This paper presents an event-based visual-inertial odometry system that addresses these limitations through intermedi...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:dnet:idus________::ce1d2c90d7f287fc3ea937e4275b9232 |
| Acceso en línea: | https://hdl.handle.net/11441/184185 https://doi.org/10.1016/j.cviu.2026.104650 |
| Access Level: | acceso abierto |
| Palabra clave: | Event camera Unmanned Aerial Vehicle Deep learning Visual-inertial odometry Sensor fusion Pose estimation |
| Sumario: | Indoor UAV navigation faces significant challenges due to GPS signal absence and limitations of conventional visual-inertial systems under challenging lighting and motion conditions. This paper presents an event-based visual-inertial odometry system that addresses these limitations through intermediate frame reconstruction from event streams combined with established odometry algorithms. The approach leverages event cameras’ unique characteristics — microsecond temporal resolution, high dynamic range (120 dB), and motion blur immunity — to maintain stable navigation performance under conditions that cause conventional systems to fail. The system achieves real-time operation at 30 Hz frame reconstruction and 20 Hz pose estimation on embedded hardware, consuming 15 W power while adding only 50 g to the UAV platform. Experimental validation in controlled indoor environments demonstrates mean absolute pose errors of 26–42 cm across different operational conditions, comparable to conventional visual-inertial systems. Critically, the system maintains stable performance during rapid lighting transitions, showing only 59% performance degradation compared to baseline conditions, while conventional cameras typically experience complete tracking failure. The results establish event-based visual-inertial odometry as a viable alternative for indoor UAV navigation, particularly in applications requiring environmental robustness over marginal accuracy improvements under optimal conditions. |
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