3D Pose Estimation in Projection Space via Pixel-level Visible Light Communication
Overview
This research introduces a method for real-time 3D pose estimation using pixel-level visible light communication (PVLC) embedded in projected imagery, enabling accurate alignment between projectors and devices without external sensing infrastructure. By formulating the problem as a Perspective-n-Point (PnP) task using four coplanar photosensors, the system achieves approximately 8 ms latency and 19.2 mm RMSE after Kalman filtering, marking the first realization of real-time 3D pose estimation coordinated with high-quality projected content for aerial robots.
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Reported quantity · latency: 8 other · Basis not reported
“imation to three dimensions, we formulate it as a Perspective-n-Point (PnP) problem. The 2D pixel positions that four coplanar photosensors on the device receive via PVLC serve as the image points, and their known 3D layout provides the object points. Experiments show that the proposed system estimates 3D pose with a latency of approximately 8~ms and a 3D position RMSE of 19.2~mm after Kalman filtering. These results”
Exact source · revision 1Source owner not reported
Reported quantity · position RMSE: 19.2 other · Basis not reported
“imation to three dimensions, we formulate it as a Perspective-n-Point (PnP) problem. The 2D pixel positions that four coplanar photosensors on the device receive via PVLC serve as the image points, and their known 3D layout provides the object points. Experiments show that the proposed system estimates 3D pose with a latency of approximately 8~ms and a 3D position RMSE of 19.2~mm after Kalman filtering. These results”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignal3D Pose Estimation in Projection Space via Pixel-level Visible Light Communication
This research introduces a method for real-time 3D pose estimation using pixel-level visible light communication (PVLC) embedded in projected imagery, enabling accurate alignment between projectors and devices without external sensing infrastructure. By formulating the problem as a Perspective-n-Point (PnP) task using four coplanar photosensors, the system achieves approximately 8 ms latency and 19.2 mm RMSE after Kalman filtering, marking the first realization of real-time 3D pose estimation coordinated with high-quality projected content for aerial robots.
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