Design and implementation of a new high-accuracy interpolation encoder IC for magneto-resistive sensors

Chen, Wenyu; Chang, I-Feng; Chao, Paul C.-P.; Thakur, Smriti; Tu, Tse-Yi

doi:10.1007/s00542-020-04949-9

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Both generally use the wheel ticks as the source but apply some moderate filtering to smooth the signal. However, the ECU on the network that applies the filtering has access to many more samples of the wheel tick counter (and may employ interpolation on the tick counter [38]) than are transmitted on the vehicle bus, so even though these signals are filtered, they are typically accurate. One must be aware, though, that to convert wheel RPMs to speeds, an accurate wheel circumference is needed.…”

Section: Typical Motion Signalsmentioning

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.

show abstract

Section: Typical Motion Signalsmentioning

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

“…Both generally use the wheel ticks as the source but apply some moderate filtering to smooth the signal. However, the ECU on the network that applies the filtering has access to many more samples of the wheel tick counter (and may employ interpolation on the tick counter [39]) than are transmitted on the vehicle bus, so even though these signals are filtered, they are typically accurate. One must be aware, though, that to convert wheel RPMs to speeds, an accurate wheel circumference is needed.…”

Section: Typical Motion Signalsmentioning

confidence: 99%

2.5D Vehicle Odometry Estimation

Eising¹,

Pereira²,

Horgan³

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract