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Visual Servoing-Based Active Vision for 3D Object Reconstruction
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DOI：10.1109/LRA.2025.3641435.png)
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Abstract
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In this letter, we present a novel dual-task, closed-loop, visual servoing-based active vision framework in an eye-in-hand configuration. The proposed active vision framework continuously drives the camera motion by coupling continuous Next-Best-View (NBV) planning and visual servo control within a unified formulation, is NBV-objective-agnostic, and enables real-time, closed-loop exploration of objects. We demonstrate how this approach can be applied to the 3D reconstruction of static volumetric objects. The approach is validated in the real world with a diverse set of relevant objects and we observe that the visual servo scheme produces smooth exploration trajectories that keep the camera focused at the object. We also show that our gradient-based continuous NBV-strategy is highly competitive with baseline strategies that leverage global viewpoint sampling and results in efficient exploration with strong object coverage.
Keywords:
Visual servoing
reactive and sensor-based planning
planning under uncertainty
RGB-D perception
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Cited Papers
MAP-NBV: Multi-agent Prediction-guided Next-Best-View Planning for Active 3D Object Reconstruction
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