AI-IMU Dead-Reckoning

By Martin Brossard, Axel Barrau, Silvere Bonnabel. IEEE Transactions on Intelligent Vehicles, 2019

299 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c80a0c6fcdadc1afe848d7089a314192897f3ab1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Qiang Xu
2. Sourabh Vora
3. Holger Caesar
4. Oscar Beijbom
5. Alex H. Lang
6. Philipp Krahenbuhl
7. Xingyi Zhou
8. Tianwei Yin

In Artificial Intelligence

1. Venice Erin Liong
2. Qiang Xu
3. Sourabh Vora
4. Holger Caesar
5. Oscar Beijbom
6. Alex H. Lang

In Electrical and Electronic Engineering

1. Qiang Xu

Citations in the corpus

• nuScenes: A Multimodal Dataset for Autonomous Driving (2019, background): “Such localization systems are vulnerable to GPS outages, as seen on the KITTI dataset [32, 7].”