We present an unsupervised deep neural network approach to the fusion of
RGB-D imagery with inertial measurements for absolute trajectory estimation.
Our network, dubbed the Visual-Inertial-Odometry Learner (VIOLearner), learns
to perform visual-inertial odometry (VIO) without inertial measurement unit
(IMU) intrinsic parameters (corresponding to gyroscope and accelerometer bias
or white noise) or the extrinsic calibration between an IMU and camera. The
network learns to integrate IMU measurements and generate hypothesis
trajectories which are then corrected online according to the Jacobians of
scaled image projection errors with respect to a spatial grid of pixel
coordinates. We evaluate our network against state-of-the-art (SOA)
visual-inertial odometry, visual odometry, and visual simultaneous localization
and mapping (VSLAM) approaches on the KITTI Odometry dataset and demonstrate
competitive odometry performance.

本研究介绍了一种无监督深度神经网络方法，用于融合 RGB-D 图像和惯性测量进行绝对轨迹估计。我们的网络在没有 IMU 固有参数或 IMU 与相机之间的外部校准的情况下进行学习，学习整合 IMU 测量并生成假设轨迹，然后根据空间像素坐标的缩放图像投影误差的雅可比矩阵进行在线更正。在 KITTI Odometry 数据集上与最先进的视觉惯性测距，视觉测距和视觉同时定位和地图构建（VSLAM）方法进行了比较，表现出有竞争力的测距性能。