Ground vehicle odometry using a non-intrusive inertial speed sensor

Shah, Het; Haldar, Siddhant; Ner, Rohit; Jha, Siddharth; Chakravarty, Debashish

doi:10.1109/icit.2019.8754998

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…Typically, road vehicles will be equipped with wheel speed sensors, for example, Hall‐effect [13, 14] or magneto resistive [15] sensors, though research continues into potentially better alternatives [16]. One sensor is deployed for each wheel.…”

Section: Introductionmentioning

confidence: 99%

2.5D vehicle odometry estimation

Eising

Pereira²,

Horgan

et al. 2021

IET Intelligent Trans Sys

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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, this is augmented by fitting a quadratic to the incoming signals, enabling interpolation, extrapolation, and a finer integration of the vehicle position. It is shown, 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, a planar model of the vehicle suspension is defined, thus augmenting the odometry model. An experimental framework and evaluations criteria is presented 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, some application results that show a performance boost for viewing and computer vision applications using the proposed odometry are presented.

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Section: Introductionmentioning

confidence: 99%

2.5D vehicle odometry estimation

Eising

Pereira²,

Horgan

et al. 2021

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…Typically, road vehicles will be equipped with wheel speed sensors, e.g., Hall-effect [14,15] or magneto resistive [16] sensors, though research continues into potentially better alternatives [17]. One sensor is deployed for each wheel.…”

Section: Introductionmentioning

confidence: 99%

2.5D Vehicle Odometry Estimation

Eising¹,

Pereira²,

Horgan³

et al. 2021

Preprint

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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.

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“…Wheel encoders are commonly deployed on vehicles (Brossard and Bonnabel, 2019) where the sensor information is broadcast on the vehicle's system bus (CAN, FlexRay, Ethernet). Typically, these encoders utilise Hall effect sensors (Popovic, 2003) though research continues into potentially better alternatives (Shah et al, 2019). To detect changes in heading, two common sensor types are deployed in vehicles: steering angle sensors (e.g.…”

Section: Introductionmentioning

confidence: 99%

A 2.5D Vehicle Odometry Estimation for Vision Applications

Moran¹,

Periera²,

Selvaraju³

et al. 2021

Preprint

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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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Ground vehicle odometry using a non-intrusive inertial speed sensor

Cited by 3 publications

References 10 publications

2.5D vehicle odometry estimation

2.5D vehicle odometry estimation

2.5D Vehicle Odometry Estimation

A 2.5D Vehicle Odometry Estimation for Vision Applications

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