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.

翻译：本文提出了一种新颖的相机运动估计方法，通过场景流整合RGB-D图像与惯性数据。我们的目标是在刚性三维环境中精确估计相机运动，同时解算惯性测量单元（IMU）的状态。所提方法具有灵活的操作模式，既可执行多帧联合优化，也可对较旧数据进行边缘化处理，从而有效利用历史观测信息。为评估方法性能，我们采用ICL-NUIM数据集的合成数据与OpenLORIS-Scene数据集的真实数据序列进行验证。结果表明，相较于仅使用视觉数据，这两种传感器的融合能显著提升相机运动估计的精度。