GALNet: An End-to-End Deep Neural Network for Ground Localization of Autonomous Cars

Ricardo Mendoza, Bingyi Cao, Daniel Goehring, Raúl Rojas

2020

Abstract

Odometry based on Inertial, Dynamic and Kinematic data (IDK-Odometry) for autonomous cars has been widely used to compute the prior estimation of Bayesian localization systems which fuse other sensors such as camera, RADAR or LIDAR. IDK-Odometry also gives the vehicle information by way of emergency when other methods are not available. In this work, we propose the use of deep neural networks to estimate the relative pose of the car given two timestamps of inertial-dynamic-kinematic data. We show that a neural network can find a solution to the optimization problem employing an approximation of the Vehicle Slip Angle (VSA). We compared our results to an IDK-Odometry system based on an Unscented Kalman Filter and Ackermann-wheel odometry. To train and test the network, we used a dataset which consists of ten driven trajectories with our autonomous car. Moreover, we successfully improved the results of the network employing collected data with a model autonomous car in order to increase the trajectories with high VSA.

Paper Citation

in Harvard Style

Mendoza R., Cao B., Goehring D. and Rojas R. (2020). GALNet: An End-to-End Deep Neural Network for Ground Localization of Autonomous Cars.In Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: ROBOVIS, ISBN 978-989-758-479-4, pages 39-50. DOI: 10.5220/0010175000390050

in Bibtex Style

@conference{robovis20,
author={Ricardo Mendoza and Bingyi Cao and Daniel Goehring and Raúl Rojas},
title={GALNet: An End-to-End Deep Neural Network for Ground Localization of Autonomous Cars},
booktitle={Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: ROBOVIS,},
year={2020},
pages={39-50},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010175000390050},
isbn={978-989-758-479-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: ROBOVIS,
TI - GALNet: An End-to-End Deep Neural Network for Ground Localization of Autonomous Cars
SN - 978-989-758-479-4
AU - Mendoza R.
AU - Cao B.
AU - Goehring D.
AU - Rojas R.
PY - 2020
SP - 39
EP - 50
DO - 10.5220/0010175000390050