AI-IMU Dead-Reckoning

Martin Brossard (1) , Axel Barrau (2) , Silvère Bonnabel (1)

1 CAOR - Centre de Robotique
2 Safran Tech

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

Mots clés: deep learning; invariant extended Kalman filter; KITTI dataset; inertial navigation; inertial measurement unit; localization

Domaines: Robotique [cs.RO]; Automatique / Robotique; Machine Learning [stat.ML]

Martin Brossard, Axel Barrau, Silvère Bonnabel. AI-IMU Dead-Reckoning. IEEE Transactions on Intelligent Vehicles, 2020, ⟨10.1109/TIV.2020.2980758⟩. ⟨hal-02097099v2⟩