Tire Radii Estimation Using a Marginalized Particle Filter

Lundquist, Christian; Karlsson, Rickard; Özkan, Emre; Gustafsson, Fredrik

doi:10.1109/tits.2013.2284930

Cited by 31 publications

(14 citation statements)

References 22 publications

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“…Methods have been developed requiring the vehicle to perform particular trajectories [8], [3] to calibrate the sensors systematic errors. Others have used particle filtering [10] or augmented state Kalman filters [4] to find the model calibration parameters. Methods estimating the parameters in post-processing by comparing odometry and GNSS relative displacements have also been presented [12].…”

Section: B Smoothingmentioning

confidence: 99%

Four-Wheeled Dead-Reckoning Model Calibration using RTS Smoothing

Welte

2019

2019 International Conference on Robotics and Automation (ICRA)

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Localization is one of the main challenges to be addressed to develop autonomous vehicles able to perform complex maneuvers on roads opened to public traffic. Having an accurate dead-reckoning system is an essential step to reach this objective. This paper presents a dead-reckoning model for carlike vehicles that performs the data fusion of complementary and redundant sensors: wheel encoders, yaw rate gyro and steering wheel measurements. In order to get an accurate deadreckoning system with a drift reduced to the minimum, the parameters have to be well calibrated and the procedure has to be simple and efficient. We present a method able to accurately calibrate the parameters without knowing the ground truth by using a Rauch-Tung-Striebel smoothing scheme which enables to obtain state estimates as close to the ground truth as possible. The smoothed estimates are then used within a optimization process to calibrate the model parameters. The method has been tested using data recorded from an experimental vehicle on public roads. The results show a significant diminution of the dead-reckoning drift compared to a commonly used calibration method. We evaluate finally the average distance a vehicle can navigate without exteroceptive sensors by using the proposed four-wheeled dead reckoning system.

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Section: B Smoothingmentioning

confidence: 99%

Four-Wheeled Dead-Reckoning Model Calibration using RTS Smoothing

Welte

2019

2019 International Conference on Robotics and Automation (ICRA)

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“…This is consistent with many navigation systems, where dead reckoning is used to decrease the state dimension. In this work, we determine v x from the wheel rotation rates given by the wheel-speed sensors and assuming a known tire radius (see, e.g., [16] for tire-radius estimation).…”

Section: Modeling and Problem Formulationmentioning

confidence: 99%

“…In this paper we assume that the virtual measurement (10) is Gaussian distributed with zero mean and a priori determined standard deviation σ virt , and we denote the full measurement vectorȳ y y k = [y y y T k φ mψvirt ] T . In practice, measurements using the wheel rotation speed have scale errors due to differences between the true and estimated wheel radius r. This is not considered here but we refer to [16] for one way to estimate the tire radii. Remark 1.…”

Section: Observabilitymentioning

confidence: 99%

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Noise-Statistics Learning of Automotive-Grade Sensors Using Adaptive Marginalized Particle Filtering

Berntorp

Cairano

2019

Journal of Dynamic Systems, Measurement, and Control

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This paper presents a method for real-time identification of sensor statistics especially aimed for low-cost automotive-grade sensors. Based on recent developments in adaptive particle filtering (PF) and under the assumption of Gaussian distributed noise, our method identifies the slowly time-varying sensor offsets and variances jointly with the vehicle state, and it extends to banked roads. While the method is primarily focused on learning the noise characteristics of the sensors, it also produces an estimate of the vehicle state. This can then be used in driver-assistance systems, either as a direct input to the control system or indirectly to aid other sensor-fusion methods. The paper contains verification against several simulation and experimental data sets. The results indicate that our method is capable of bias-free estimation of both the bias and the variance of each sensor, that the estimation results are consistent over different data sets, and that the computational load is feasible for implementation on computationally limited embedded hardware typical of automotive applications.

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“…Based on SIS and resampling, the SIR filter and its further extension have been applied for various parameter estimation for the state space model since they relaxes linearity and Gaussian assumptions; see some very recent work e.g. [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

On the Bias of the SIR Filter in Parameter Estimation of the Dynamics Process of State Space Models

Rodrı́guez

Bajo

et al. 2015

Distributed Computing and Artificial Intelligence, 12th International Conference

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As a popular nonlinear estimation tool, the sampling importance resampling (SIR) filter has been applied with the expectation-maximization (EM) principle, including the typical maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation, for estimating the parameters of the state space model (SSM). This paper concentrates on an inevitable bias existing in the EM-SIR filter for estimating the dynamics process of the SSM. It is analyzed that the root reason for the bias is the sample impoverishment caused by the resampling procedure employed in the filter. A process noise simulated for the particle propagation that is larger than the real noise involved with the true state will be helpful to counteract sample impoverishment, thereby providing better filtering result. Correspondingly, the EM-SIR filter tends to yield a biased (larger-than-the-truth) estimate of the process noise if it is unknown and needs to be estimated. The bias is elaborated via a straightforward roughening approach by means of both qualitative logical deduction and quantitative numerical simulation. However, it seems hard to fully remove this bias in practice.

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Tire Radii Estimation Using a Marginalized Particle Filter

Cited by 31 publications

References 22 publications

Four-Wheeled Dead-Reckoning Model Calibration using RTS Smoothing

Four-Wheeled Dead-Reckoning Model Calibration using RTS Smoothing

Noise-Statistics Learning of Automotive-Grade Sensors Using Adaptive Marginalized Particle Filtering

On the Bias of the SIR Filter in Parameter Estimation of the Dynamics Process of State Space Models

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