Camera Motion Estimation from RGB-D-Inertial Scene Flow
This work addresses camera motion estimation for robotics or AR/VR applications, presenting an incremental improvement by fusing sensors.
The paper tackles camera motion estimation in rigid 3D environments by integrating RGB-D images and inertial data through scene flow, resulting in enhanced accuracy compared to using visual data alone, as demonstrated on synthetic and real datasets.
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.