Camera Motion Estimation from RGB-D-Inertial Scene Flow
arxiv(2024)
摘要
In this paper, we introduce a novel formulation for camera motion estimation
that integrates RGB-D images and inertial data through scene flow. Our goal is
to accurately estimate the camera motion in a rigid 3D environment, along with
the state of the inertial measurement unit (IMU). Our proposed method offers
the flexibility to operate as a multi-frame optimization or to marginalize
older data, thus effectively utilizing past measurements. To assess the
performance of our method, we conducted evaluations using both synthetic data
from the ICL-NUIM dataset and real data sequences from the OpenLORIS-Scene
dataset. Our results show that the fusion of these two sensors enhances the
accuracy of camera motion estimation when compared to using only visual data.
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