BooMVio: Bootstrapped Monocular VisualInertial Odometry with Absolute Trajectory Estimation through Unsupervised Deep Learning Machine learning has emerged as an extraordinary tool for solving many computer vision tasks by extracting and correlating meaningful features from high dimensional inputs in ways that often exceed the best humanderived modeling efforts. However, the area of visionaided localization remains diverse with many traditional, modelbased approaches (i.e. filtering or nonlinear least squares based) often outperforming deep, modelfree approaches. In this work, we present Bootstrapped Monocular VIO (BooM), a scaled monocular visualinertial odometry (VIO) solution that leverages the complex data association ability of modelfree approaches with the ability to exploit known geometric dynamics with modelbased approaches. Our endtoend, unsupervised deep neural network simultaneously learns to perform visualinertial odometry and estimate scene depth while scale is enforced through a loss signal computed from position change magnitude estimates from traditional methods. We evaluate our network against a stateoftheart (SoA) approach on the KITTI driving dataset as well as a micro aerial vehicle (MAV) dataset that we collected in the AirSim simulation environment. We further demonstrate the benefits of our combined approach through robustness tests on degraded trajectories.

DeepVIO: Selfsupervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Constraints This paper presents an selfsupervised deep learning network for monocular visual inertial odometry (named DeepVIO). DeepVIO provides absolute trajectory estimation by directly merging 2D optical flow feature (OFF) and Inertial Measurement Unit (IMU) data. Specifically, it firstly estimates the depth and dense 3D point cloud of each scene by using stereo sequences, and then obtains 3D geometric constraints including 3D optical flow and 6DoF pose as supervisory signals. Note that such 3D optical flow shows robustness and accuracy to dynamic objects and textureless environments. In DeepVIO training, 2D optical flow network is constrained by the projection of its corresponding 3D optical flow, and LSTMstyle IMU preintegration network and the fusion network are learned by minimizing the loss functions from egomotion constraints. Furthermore, we employ an IMU status update scheme to improve IMU pose estimation through updating the additional gyroscope and accelerometer bias. The experimental results on KITTI and EuRoC datasets show that DeepVIO outperforms stateoftheart learning based methods in terms of accuracy and data adaptability. Compared to the traditional methods, DeepVIO reduces the impacts of inaccurate CameraIMU calibrations, unsynchronized and missing data.