AI-IMU Dead-Reckoning

TL;DR

This paper introduces a novel IMU-based dead-reckoning method for wheeled vehicles using Kalman filtering and neural networks, achieving high accuracy comparable to LiDAR or stereo vision methods.

Contribution

The paper presents a new IMU-only dead-reckoning approach that dynamically adapts noise parameters with neural networks and self-calibrates biases, improving accuracy.

Findings

Achieves 1.10% average translational error on KITTI dataset.

Competitively matches top methods using LiDAR or stereo vision.

Provides open-source implementation for reproducibility.

Abstract

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