This paper proposes a method to estimate the pose of a sensor mounted on a vehicle as the vehicle moves through the world, an important topic for autonomous driving systems. Based on a set of commonly deployed vehicular odometric sensors, with outputs available on automotive communication buses (e.g. CAN or FlexRay), we describe a set of steps to combine a planar odometry based on wheel sensors with a suspension model based on linear suspension sensors. The aim is to determine a more accurate estimate of the camera pose. We outline its usage for applications in both visualisation and computer vision.
@article{arxiv.2105.02679,
title = {A 2.5D Vehicle Odometry Estimation for Vision Applications},
author = {Paul Moran and Leroy-Francisco Periera and Anbuchezhiyan Selvaraju and Tejash Prakash and Pantelis Ermilios and John McDonald and Jonathan Horgan and Ciarán Eising},
journal= {arXiv preprint arXiv:2105.02679},
year = {2021}
}