A 2.5D Vehicle Odometry Estimation for Vision Applications

• URL: http://arxiv.org/abs/2105.02679v1
• Date: Thu, 6 May 2021 14:01:46 GMT
• Title: A 2.5D Vehicle Odometry Estimation for Vision Applications
• Authors: Paul Moran, Leroy-Francisco Periera, Anbuchezhiyan Selvaraju, Tejash Prakash, Pantelis Ermilios, John McDonald, Jonathan Horgan, Ciar\'an Eising
• Abstract summary: 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.
• Score: 0.2069421313572092
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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