# 2.5D Vehicle Odometry Estimation

arXiv: https://arxiv.org/abs/2111.08398 · HTML: https://mlsanity.com/paper/2111.08398/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving](https://mlsanity.com/paper/1905.01489/) (2019)
- [Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline](https://mlsanity.com/paper/2012.02124/) (2020)
- [Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras](https://mlsanity.com/paper/2103.17001/) (2021)
- [Computer vision in automated parking systems: Design, implementation and challenges](https://mlsanity.com/paper/2104.12537/) (2021)
- [Spherical formulation of geometric motion segmentation constraints in fisheye cameras](https://mlsanity.com/paper/2104.12404/) (2021)
- [Ground vehicle odometry using a non-intrusive inertial speed sensor](https://mlsanity.com/paper/1809.06472/) (2018)
- [Development of a multi-sensor perceptual system for mobile robot and EKF-based localization](https://mlsanity.com/paper/1611.09424/) (2016)
- [A 2.5D Vehicle Odometry Estimation for Vision Applications](https://mlsanity.com/paper/2105.02679/) (2021)

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

- [Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges](https://mlsanity.com/paper/2205.13281/) (2022)

## Abstract (arXiv)

It is well understood that in ADAS applications, a good estimate of the pose of the vehicle is required. This paper proposes a metaphorically named 2.5D odometry, whereby the planar odometry derived from the yaw rate sensor and four wheel speed sensors is augmented by a linear model of suspension. While the core of the planar odometry is a yaw rate model that is already understood in the literature, we augment this by fitting a quadratic to the incoming signals, enabling interpolation, extrapolation, and a finer integration of the vehicle position. We show, by experimental results with a DGPS/IMU reference, that this model provides highly accurate odometry estimates, compared with existing methods. Utilising sensors that return the change in height of vehicle reference points with changing suspension configurations, we define a planar model of the vehicle suspension, thus augmenting the odometry model. We present an experimental framework and evaluations criteria by which the goodness of the odometry is evaluated and compared with existing methods. This odometry model has been designed to support low-speed surround-view camera systems that are well-known. Thus, we present some application results that show a performance boost for viewing and computer vision applications using the proposed odometry