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This research addresses localization drift in visual-inertial odometry (VIO) by enabling UAVs to actively create visual features on demand, such as dropping markers or touching walls, to enhance navigation in texture-poor environments. The proposed framework demonstrates a 35% reduction in position error compared to baseline systems, offering a novel approach to maintaining sensor accuracy without relying on external maps or additional hardware.
Not Losing Your Way: Mitigating Visual-Inertial Odometry Drift through On-Demand Visual Feature Creation
arXiv:2611.07687v1 Announce Type: new Abstract: Visual-inertial odometry (VIO) can degrade during aerial inspection in repetitive or poorly textured environments. We propose an active environment-modification framework for unmanned aerial vehicles (UAVs) relying on cameras and an inertial measurement unit only, without prior maps or additional localization sensors. Inspired by the fairy tale of Tom Thumb, the UAV creates visual features on demand by releasing ground markers or marking walls through physical contact. An information-based indicator triggers interventions using temporal confirmation and hysteresis. Evaluation includes three simulated environments and a 900-run factorial campaign. Lower cruise speed and active marking are associated with reduced divergence, while accuracy among non-divergent runs depends on speed-strategy interactions. Corridor experiments, with five runs per configuration, use OptiTrack for flight control while VIO-based marking decisions remain active online. The combined framework reduces the reported position root mean square error by approximately 35% relative to baseline VIO, supporting active feature creation for drift mitigation.
Abstract:Visual-inertial odometry (VIO) can degrade during aerial inspection in repetitive or poorly textured environments. We propose an active environment-modification framework for unmanned aerial vehicles (UAVs) relying on cameras and an inertial measurement unit only, without prior maps or additional localization sensors. Inspired by the fairy tale of Tom Thumb, the UAV creates visual features on demand by releasing ground markers or marking walls through physical contact. An information-based indicator triggers interventions using temporal confirmation and hysteresis. Evaluation includes three simulated environments and a 900-run factorial campaign. Lower cruise speed and active marking are associated with reduced divergence, while accuracy among non-divergent runs depends on speed-strategy interactions. Corridor experiments, with five runs per configuration, use OptiTrack for flight control while VIO-based marking decisions remain active online. The combined framework reduces the reported position root mean square error by approximately 35% relative to baseline VIO, supporting active feature creation for drift mitigation.
8 pages, 4 figures. Submitted to the 2027 IEEE International Conference on Robotics and Automation (ICRA 2027)
Robotics (cs.RO)
arXiv:2611.07687 [cs.RO]
(or arXiv:2611.07687v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2611.07687 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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