# A 2.5D Vehicle Odometry Estimation for Vision Applications

arXiv: https://arxiv.org/abs/2105.02679 · HTML: https://mlsanity.com/paper/2105.02679/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 6 May 2021.

## Builds on (most cited first, 3 of 3)

- [Visual-Inertial Odometry of Aerial Robots](https://mlsanity.com/paper/1906.03289/) (2019)
- [Ground vehicle odometry using a non-intrusive inertial speed sensor](https://mlsanity.com/paper/1809.06472/) (2018)
- [Spherical formulation of moving object geometric constraints for monocular fisheye cameras](https://mlsanity.com/paper/2003.03262/) (2020)

## Built on by (most cited first, 1 of 1)

- [2.5D Vehicle Odometry Estimation](https://mlsanity.com/paper/2111.08398/) (2021)

## Abstract (arXiv)

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.