Accurate calibration of systematic errors for car-like mobile robots using experimental orientation errors

Jung, Daun; Seong, Jihoon; Moon, Chang Bae; Jin, Jiyong; Chung, Woojin

doi:10.1007/s12541-016-0135-4

Cited by 20 publications

(7 citation statements)

References 9 publications

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“…Some works, as [10], [17], [44], use information from additional encoders or encoders coupled in freewheels to decrease pose uncertainty. In [16], odometry uncertainty in car-like mobile robot is investigated.…”

Section: Other Approachesmentioning

confidence: 99%

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The Impact of Parametric Uncertainties on Mobile Robots Velocities and Pose Estimation

et al. 2019

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This paper analyses the impact of parametric uncertainties of a mobile robot kinematic model on velocity and pose estimation, providing models and quantifiable knowledge about them. Most works neglect how the uncertainty regarding the robot's construction aspects (such as wheels radii, the distance between them, and the robot's center of mass) affects both velocities and pose estimation. To help readers understand the influence of such parametric uncertainties, we performed experiments in a simulator and used the collected data on the proposed models. The paper also analyses how the magnitude of velocities considered by the controllers and the followed path can decrease or increase the impact of the parametric uncertainties. The proposed models and presented analysis help understanding the influence (isolated or simultaneous) of different sources of uncertainty in the robot's velocities and pose estimation. This knowledge can be applied to estimate uncertainties for localization methods based on data fusion, complementing or even avoiding the experimental procedures. Also, the development of controllers, robot simulators and new methodologies for parameters' calibration and the design and construction of new robots can also profit from the results presented in this paper.

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Section: Other Approachesmentioning

confidence: 99%

“…There are several methods [11]- [16] based on experimental setups that can be used to estimate position uncertainty. These experimental evaluations of the uncertainties usually require many trials and, sometimes, another localization system to estimate the robot position.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Parametric Uncertainties on Mobile Robots Velocities and Pose Estimation

et al. 2019

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“…Jung [14] proposed a method that uses an oval track to accurately calibrate the odometry motion model based on the vehicle structure. For calibration, Jung suggests that the systematic errors comprise two representative variables: the wheelbase and the wheel diameter.…”

Section: Integration Of Odometry and Gnssmentioning

confidence: 99%

Practical Modeling of GNSS for Autonomous Vehicles in Urban Environments

Lee

Cho

Hyeong

et al. 2019

Sensors

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Autonomous navigation technology is used in various applications, such as agricultural robots and autonomous vehicles. The key technology for autonomous navigation is ego-motion estimation, which uses various sensors. Wheel encoders and global navigation satellite systems (GNSSs) are widely used in localization for autonomous vehicles, and there are a few quantitative strategies for handling the information obtained through their sensors. In many cases, the modeling of uncertainty and sensor fusion depends on the experience of the researchers. In this study, we address the problem of quantitatively modeling uncertainty in the accumulated GNSS and in wheel encoder data accumulated in anonymous urban environments, collected using vehicles. We also address the problem of utilizing that data in ego-motion estimation. There are seven factors that determine the magnitude of the uncertainty of a GNSS sensor. Because it is impossible to measure each of these factors, in this study, the uncertainty of the GNSS sensor is expressed through three variables, and the exact uncertainty is calculated. Using the proposed method, the uncertainty of the sensor is quantitatively modeled and robust localization is performed in a real environment. The approach is validated through experiments in urban environments.

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“…In ref. [ 42 ] the same robot and path are applied, but the experimental orientation error is utilized at the end of the path instead of the position error to eliminate the small-angle approximation. The four-wheel-steering of small RC cars is calibrated with a sequential method in ref.…”

Section: Introductionmentioning

confidence: 99%

Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration

Fazekas

Gáspár

Németh

2021

Sensors

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Localization is a key part of an autonomous system, such as a self-driving car. The main sensor for the task is the GNSS, however its limitations can be eliminated only by integrating other methods, for example wheel odometry, which requires a well-calibrated model. This paper proposes a novel wheel odometry model and its calibration. The parameters of the nonlinear dynamic system are estimated with Gauss–Newton regression. Due to only automotive-grade sensors are applied to reach a cost-effective system, the measurement uncertainty highly corrupts the estimation accuracy. The problem is handled with a unique Kalman-filter addition to the iterative loop. The experimental results illustrate that without the proposed improvements, in particular the dynamic wheel assumption and integrated filtering, the model cannot be calibrated precisely. With the well-calibrated odometry, the localization accuracy improves significantly and the system can be used as a cost-effective motion estimation sensor in autonomous functions.

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Accurate calibration of systematic errors for car-like mobile robots using experimental orientation errors

Cited by 20 publications

References 9 publications

The Impact of Parametric Uncertainties on Mobile Robots Velocities and Pose Estimation

The Impact of Parametric Uncertainties on Mobile Robots Velocities and Pose Estimation

Practical Modeling of GNSS for Autonomous Vehicles in Urban Environments

Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration

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