  In this paper we propose a novel accurate method for dead-reckoning of
wheeled vehicles based only on an Inertial Measurement Unit (IMU). In the
context of intelligent vehicles, robust and accurate dead-reckoning based on
the IMU may prove useful to correlate feeds from imaging sensors, to safely
navigate through obstructions, or for safe emergency stops in the extreme case
of exteroceptive sensors failure. The key components of the method are the
Kalman filter and the use of deep neural networks to dynamically adapt the
noise parameters of the filter. The method is tested on the KITTI odometry
dataset, and our dead-reckoning inertial method based only on the IMU
accurately estimates 3D position, velocity, orientation of the vehicle and
self-calibrates the IMU biases. We achieve on average a 1.10% translational
error and the algorithm competes with top-ranked methods which, by contrast,
use LiDAR or stereo vision. We make our implementation open-source at:
https://github.com/mbrossar/ai-imu-dr
