Online calibration of vehicle powertrain and pose estimation parameters using integrated dynamics

Seegmiller, Neal; Rogers-Marcovitz, Forrest; Kelly, Alonzo

doi:10.1109/icra.2012.6225110

Cited by 5 publications

(6 citation statements)

References 15 publications

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“…This paper unifies and elaborates on previous publications by the authors: Kelly (2004a) applied IPEM to 2D odometry calibration, Rogers-Marcovitz and applied IPEM to slip model calibration, Seegmiller et al (2011) demonstrated slip model calibration in 3D on Crusher and introduced stochastic calibration, and later applied IPEM to powertrain model and sensor calibration (Seegmiller et al, 2012). Note that the name "IPEM" was not used in previous papers, but was chosen for this journal paper.…”

Section: Related Workmentioning

confidence: 73%

“…When velocity commands change, the response of many powertrains can be modeled adequately by a time delay and a first-order transient response. In Seegmiller et al (2012), the authors present how the IPEM formulation can be used to identify the time delay and time constant. A brief summary is presented here, but please refer to the original paper for a full discussion of results and related work.…”

Section: Application: Identifying Powertrain Dynamicsmentioning

confidence: 99%

“…Experimental results are provided for odometry calibration on an indoor mobile robot (Section 3) and slip calibration on off-road skid and Ackerman steered platforms (Section 5). In another publication, the authors validated the approach on an articulated rover (Seegmiller et al, 2012) (see also Section 6.4). The calibration approach may be useful on other platforms and terrain conditions not tested here, such as automobiles on paved roads.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle model identification by integrated prediction error minimization

Seegmiller

Rogers-Marcovitz

Miller

et al. 2013

The International Journal of Robotics Research

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We present a highly effective approach for the calibration of vehicle models. The approach combines the output error technique of system identification theory and the convolution integral solutions of linear systems and stochastic calculus. Rather than calibrate the system differential equation directly for unknown parameters, we calibrate its first integral. This integrated prediction error minimization (IPEM) approach is advantageous because it requires only low-frequency observations of state, and produces unbiased parameter estimates that optimize simulation accuracy for the chosen time horizon. We address the calibration of models that describe both systematic and stochastic dynamics, such that uncertainties can be computed for model predictions. We resolve numerous implementation issues in the application of IPEM, such as the efficient linearization of the dynamics integral with respect to parameters, the treatment of uncertainty in initial conditions, and the formulation of stochastic measurements and measurement covariances. While the technique can be used for any dynamical system, we demonstrate its usefulness for the calibration of wheeled vehicle models used in control and estimation. Specifically we calibrate models of odometry, powertrain dynamics, and wheel slip as it affects body frame velocity. Experimental results are provided for a variety of indoor and outdoor platforms.

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Section: Related Workmentioning

confidence: 73%

Section: Application: Identifying Powertrain Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle model identification by integrated prediction error minimization

Seegmiller

Rogers-Marcovitz

Miller

et al. 2013

The International Journal of Robotics Research

Self Cite

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“…Using the slip-compensated vehicle model, we can achieve improved dead-reckoning relative to naive integration of the odometry measurements (3). By assumption, the perturbative (i.e.…”

Section: Odometry Measurementmentioning

confidence: 99%

“…We use Integrated Perturbative Dynamics (IPD) to learn the mapping between control inputs and resultant vehicle motion by calibrating the vehicle's mobility model online using an extended Kalman filter. Past work has used IPD to identify systematic and stochastic models of wheel slip [2], powertrain dynamics, and odometry parameters [3].…”

Section: Introductionmentioning

confidence: 99%

Aiding off-road inertial navigation with high performance models of wheel slip

Rogers-Marcovitz

George

Seegmiller

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-When GPS, or other absolute positioning, is unavailable, terrain-relative velocity is crucial for dead reckoning and the vehicle's pose estimate. Unfortunately, the positiondenied accuracy of the inertial navigation system (INS) is governed by the performance of the linear velocity aiding sources, such as wheel odometry, which are typically corrupted by large systematic errors due to wheel slip. As a result, affordable terrestrial inertial navigation is ineffective in estimating position when denied position fixes for an extended period of time. For mobile robots, the mapping between inputs and resultant behavior depends critically on terrain conditions which vary significantly over time and space which cannot be pre-programmed. Past work has used Integrated Perturbative Dynamics (IPD) to identify successively systematic and stochastic models of wheel slip, but treated the pose filter only as input without improving the odometry measurements used for vehicle navigation. We present a unique approach of a predictive vehicle slip model in a delayed state extended Kalman filter. The relative pose difference between the current state and delayed state is used as a measurement update to the vehicle slip model. These results create an opportunity to compensate for wheel slip effects in terrestrial inertial navigation. This paper presents the design, calibration, and verification of such a system and concludes that the position-denied performance of the compensated system is far superior.

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Parameter Identification of a Planetary Rover Wheel–Soil Contact Model via a Bayesian Approach

Gallina

Krenn

Scharringhausen

et al. 2013

Journal of Field Robotics

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Soft soil contact models developed for planetary exploration rovers play an important role in the study of rover mobility. Nowadays, most of the existing contact models are based on Bekker theory, which requires the evaluation of several soil parameters usually measured via bevameter tests. However, substantial differences existing between the plate–soil contact scenario and the wheel–soil contact scenario, along with large variability associated with the bevameter experiments, give rise to large uncertainty in the choice of the model parameter values. In this paper, a Bayesian procedure is proposed to deal effectively with the presence of uncertainty. In the proposed approach, model parameters are random variables with prior distributions derived from bevameter measurements. The prior distributions are then enhanced to posterior distributions through single wheel test data. At the end, the procedure identifies a set of possible model parameter configurations that result in high experimental‐numerical matching.

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Online calibration of vehicle powertrain and pose estimation parameters using integrated dynamics

Cited by 5 publications

References 15 publications

Vehicle model identification by integrated prediction error minimization

Vehicle model identification by integrated prediction error minimization

Aiding off-road inertial navigation with high performance models of wheel slip

Parameter Identification of a Planetary Rover Wheel–Soil Contact Model via a Bayesian Approach

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