A 2.5D Vehicle Odometry Estimation for Vision Applications

TL;DR

This paper introduces a novel 2.5D vehicle odometry method combining wheel and suspension sensors to improve camera pose estimation for autonomous driving applications.

Contribution

It presents a new sensor fusion approach that integrates vehicular odometry and suspension data for more accurate pose estimation.

Findings

Enhanced pose accuracy over traditional methods

Effective integration of wheel and suspension sensors

Applicable to visualization and computer vision tasks

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.