Online Identification of Odometer Parameters of a Mobile Robot

Dogruer, C.U.

doi:10.1007/978-3-319-07995-0_20

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Several strategies have been proposed to improve distance sensor performance and accuracy through statistical manipulation of their measurement processes. For example, the statistical sensor model for ultrasound sensors was presented in [5] with calibration algorithms in [6] and a good review on odometer calibration is presented in [7]. As a generic definition, statistical sensor calibration is the process of improving sensor accuracy and/or precision through transforming the measurements into something closer to ground truth via combining information about the same sensed quantity that is obtained using different sources of information like ground truth data.…”

Section: Literature Reviewmentioning

confidence: 99%

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Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions

Alhashimi

Magnusson

Knorn

et al. 2020

Preprint

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We consider the problem of calibrating distance measurement of Light Detection and Ranging (lidar) sensor without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground, and so that its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. We moreover consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that “fixed features shall have fixed relative distances and angles”. The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploying special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyse their statistical performance both in simulation and with field tests, reporting thus the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observing that in field tests the approach can lead to a ten-fold improvement in the accuracy of the raw measurements.

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Section: Literature Reviewmentioning

confidence: 99%

“…The computations above assume the full knowledge of the sensor positions s k = [ x k y k ]. Since this information is not available, the best we can do is to plug in, instead, the expected sensor positions [x k y k ] in (7). Replacing [ x k y k ] with [x k y k ] in the least squares problem (18) leads thus to the problem…”

mentioning

confidence: 99%

Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions

Alhashimi

Magnusson

Knorn

et al. 2020

Preprint

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“…Eventually, a good literature review on odometer calibration is presented in [21]. We notice that this review presents, among others, an on-line identification algorithm for the odometer parameters for a differential drive mobile robot.…”

Section: Literature Reviewmentioning

confidence: 99%

Calibrating Distance Sensors for Terrestrial Applications Without Groundtruth Information

2017

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Abstract-This paper describes a new calibration procedure for distance sensors that does not require independent sources of groundtruth information, i.e., that is not based on comparing the measurements from the uncalibrated sensor against measurements from a precise device assumed as the groundtruth. Alternatively, the procedure assumes that the uncalibrated distance sensor moves in space on a straight line in an environment with fixed targets, so that the intrinsic parameters of the statistical model of the sensor readings are calibrated without requiring tests in controlled environments, but rather in environments where the sensor follows linear movement and objects do not move.The proposed calibration procedure exploits an approximated EM scheme on top of two ingredients: an heteroscedastic statistical model describing the measurement process, and a simplified dynamical model describing the linear sensor movement. The procedure is designed to be capable of not just estimating the parameters of one generic distance sensor, but rather integrating the most common sensors in robotic applications, such as Lidars, odometers and sonar rangers and learn the intrinsic parameters of all these sensors simultaneously.Tests in a controlled environment led to a reduction of the MSE of the measurements returned by a commercial triangulation Lidar by a factor between 3 and 6, comparable to the efficiency of other state-of-the art groundtruth-based calibration procedures. Adding odometric and ultrasonic information further improved the performance index of the overall distance estimation strategy by a factor of up to 1.2. Tests also show high robustness against violating the linear movements assumption.

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“…Several strategies have been proposed to improve distance sensor performance and accuracy through statistical manipulation of their measurement processes. For example, the statistical sensor model for ultrasound sensors was presented in [ 6 ] with calibration algorithms in [ 7 ] and a good review on odometer calibration is presented in [ 8 ]. As a generic definition, statistical sensor calibration is the process of improving sensor accuracy and/or precision through transforming the measurements into something closer to ground truth via combining information about the same sensed quantity that is obtained using different sources of information like ground truth data.…”

Section: Introductionmentioning

confidence: 99%

Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions

Alhashimi

Magnusson

Knorn

et al. 2020

Sensors

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We consider the problem of calibrating range measurements of a Light Detection and Ranging (lidar) sensor that is dealing with the sensor nonlinearity and heteroskedastic, range-dependent, measurement error. We solved the calibration problem without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground. Then, its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. Moreover, we consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that “fixed features shall have fixed relative distances and angles”. The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploy special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyze their statistical performance both in simulation and with field tests. We report the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observe that in field tests the approach can lead to a tenfold improvement in the accuracy of the raw measurements.

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Online Identification of Odometer Parameters of a Mobile Robot

Cited by 5 publications

References 11 publications

Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions

Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions

Calibrating Distance Sensors for Terrestrial Applications Without Groundtruth Information

Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions

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