2.5D Vehicle Odometry Estimation

TL;DR

This paper introduces a 2.5D vehicle odometry method combining planar odometry with suspension modeling, resulting in highly accurate vehicle pose estimates for ADAS applications, validated through experimental results.

Contribution

The paper presents a novel 2.5D odometry model that integrates suspension dynamics into planar odometry, improving accuracy over existing methods.

Findings

High-accuracy odometry demonstrated with DGPS/IMU reference

Enhanced vehicle pose estimation for low-speed surround-view systems

Improved performance in computer vision applications using the proposed model

Abstract

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