Simultaneous maximum-likelihood calibration of odometry and sensor parameters

Censi, Andrea; Marchionni, Luca; Oriolo, Giuseppe

doi:10.1109/robot.2008.4543516

Cited by 49 publications

(41 citation statements)

References 18 publications

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“…This example shows that it is much easier to estimate the tire radii difference between the left and the right wheel than to estimate the sum and thereby the absolute value of them. The time varying parameters δ 3 and δ 4 are integrated in the motion model (7). One common approach in joint state and parameter estimation is to augment the state vector with the unknown parameters [15].…”

Section: Modelmentioning

confidence: 99%

“…A nonlinear observer approach to estimate the tire radius is presented in [4], and a second order sliding mode observer is used to estimate the tire radius in [5] and [6]. A simultaneous maximum likelihood calibration and sensor position and orientation estimation approach for mobile robots is presented in [7], where among other parameters the wheel radii are estimated. An observer based fault detection algorithm, which detects tire radius errors using yaw rate measurement and a bicycle model of the vehicle, is described in [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tire Radii Estimation Using a Marginalized Particle Filter

Lundquist

Karlsson²,

Özkan

et al. 2014

IEEE Trans. Intell. Transport. Syst.

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Abstract-In this work measurements of individual wheel speeds and absolute position from a global positioning system (GPS) are used for high-precision estimation of vehicle tire radii. The radii deviation from its nominal value is modeled as a Gaussian random variable and included as noise components in a simple vehicle motion model. The novelty lies in a Bayesian approach to estimate online both the state vector and the parameters representing the process noise statistics using a marginalized particle filter. Field tests show that the absolute radius can be estimated with sub-millimeter accuracy. The approach is tested in accordance with the ECE R-64 regulation on a large data set (22 tests, using two vehicles and 12 different tire sets), where tire deflations are detected successfully, with high robustness, i.e., no false alarms. The proposed marginalized particle filter approach outperforms common Kalman filter based methods used for joint state and parameter estimation when compared with respect to accuracy and robustness.

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Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tire Radii Estimation Using a Marginalized Particle Filter

Lundquist

Karlsson²,

Özkan

et al. 2014

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…이후 Antonelli [11] 는 추가적인 연구를 통해 오도메트리 모델을 3개의 파라미터로 모델링하고 보 정하는 기법을 제안하였다. 최근에는 Censi [12] , Antonelli [13] 가 오도메트리와 로봇프레임에 대한 센서의 위치를 동시에 보정하는 기법을 제안하였다.…”

Section: 오도메트리의 오차 원인은 크게 시스템적 오차와 비시unclassified

Design of Experimental Test Tracks for Odometry Calibration of Wheeled Mobile Robots

Jung¹,

Moon²,

Jung³

et al. 2014

The Journal of Korea Robotics Society

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Odometry using wheel encoder is a common relative positioning technique for wheeled mobile robots. The major drawback of odometry is that the kinematic modeling errors are accumulated when the travel distance increases. Therefore, accurate calibration of odometry is required. In several related works, various schemes for odometry calibration are proposed. However, design guidelines of test tracks for odometry calibration were not considered. More accurate odometry calibration results can be achieved by using appropriate test track because the position and orientation errors after the test are affected by the test track. In this paper, we propose the design guidelines of test tracks for odometry calibration schemes using experimental heading errors. Numerical simulations and experiments clearly demonstrate that the proposed design guidelines result in more accurate calibration results.

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“…Censi et al [18] proposed a technique similar to our method. They construct a least squares calibration problem that estimates both the kinematic parameters and the sensor position.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous Parameter Calibration, Localization, and Mapping

2012

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The calibration parameters of a mobile robot play a substantial role in navigation tasks. Often these parameters are subject to variations that depend either on changes in the environment or on the load of the robot. In this paper, we propose an approach to simultaneously estimate a map of the environment, the position of the on-board sensors of the robot, and its kinematic parameters.Our method requires no prior knowledge about the environment and relies only on a rough initial guess of the parameters of the platform. The proposed approach estimates the parameters on-line and it is able to adapt to non-stationary changes of the configuration. We tested our approach in simulated environments and on a wide range of real world data using different types of robotic platforms.

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Simultaneous maximum-likelihood calibration of odometry and sensor parameters

Cited by 49 publications

References 18 publications

Tire Radii Estimation Using a Marginalized Particle Filter

Tire Radii Estimation Using a Marginalized Particle Filter

Design of Experimental Test Tracks for Odometry Calibration of Wheeled Mobile Robots

Simultaneous Parameter Calibration, Localization, and Mapping

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