Not Losing Your Way: Mitigating Visual-Inertial Odometry Drift through On-Demand Visual Feature Creation
Overview
This research proposes an active environment-modification framework for UAVs that mitigates visual-inertial odometry (VIO) drift in repetitive or poorly textured environments by creating visual features on demand.
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Environment: simulation
“ates 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-st”
Exact source · revision 1Source owner not reported
Reported quantity · runs: 900 other · reported trials · Differing source assertions
“ates 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-st”
Exact source · revision 1Source owner not reported
Reported quantity · runs: 5 other · reported trials · Differing source assertions
“rategy 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.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalNot Losing Your Way: Mitigating Visual-Inertial Odometry Drift through On-Demand Visual Feature Creation
This research proposes an active environment-modification framework for UAVs that mitigates visual-inertial odometry (VIO) drift in repetitive or poorly textured environments by creating visual features on demand.
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