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:
this https URL

本文提出了一种新的基于惯性测量单元（IMU）的死推方法，关键组件为卡尔曼滤波器和深度神经网络，该方法在 KITTI 测距数据集上得到了测试，能够准确地估计车辆的 3D 位置、速度和方向，并自校准 IMU 偏差。