AI-IMU Dead-Reckoning

• Published in: IEEE transactions on intelligent vehicles Vol. 5; no. 4; pp. 585 - 595
• Main Authors: Brossard, Martin, Barrau, Axel, Bonnabel, Silvere
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Artificial neural networks; Automatic; Computer Science; Dead reckoning; Deep learning; Engineering Sciences; Image sensors; inertial measurement unit (IMU); Inertial navigation; Inertial platforms; Intelligent vehicles; invariant extended Kalman filter; Kalman filters; KITTI dataset; Localization; Machine Learning; Obstructions; Odometers; Robotics; Sensors; Statistics; Vehicles; inertial navigation; inertial measurement unit; localization; deep learning; invariant extended Kalman filter; KITTI dataset
• ISSN: 2379-8858; 2379-8904
• DOI: 10.1109/TIV.2020.2980758

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.