Systematic Error Modeling and Bias Estimation

Zhang, Feihu; Knoll, Alois

doi:10.3390/s16050729

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…More details could be found in Zhang and Knoll ( 2016 ) in the same manner, the complete cumulative errors are, therefore, calculated. In the next section, the Dijkstra-based global exploration method will first be used to traverse the map and determine the error-minimizing path for each location by evaluating the error of each path, thus achieving the task of reducing path drift.…”

Section: Methodological Backgroundmentioning

confidence: 99%

Path Planning in Localization Uncertaining Environment Based on Dijkstra Method

et al. 2022

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Path planning obtains the trajectory from one point to another with the robot's kinematics model and environment understanding. However, as the localization uncertainty through the odometry sensors is inevitably affected, the position of the moving path will deviate further and further compared to the original path, which leads to path drift in GPS denied environments. This article proposes a novel path planning algorithm based on Dijkstra to address such issues. By combining statistical characteristics of localization error caused by dead-reckoning, the replanned path with minimum cumulative error is generated with uniforming distribution in the searching space. The simulation verifies the effectiveness of the proposed algorithm. In a real scenario with measurement noise, the results of the proposed algorithm effectively reduce cumulative error compared to the results of the conventional planning algorithm.

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Section: Methodological Backgroundmentioning

confidence: 99%

Path Planning in Localization Uncertaining Environment Based on Dijkstra Method

et al. 2022

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“…The latter inaccuracy can be explained by the fact that, according to the literature, the kyphosis angle should be calculated between C7, T-apex and T11, but no perfectly equivalent landmarks are available on the Azure Kinect skeleton. In general, measurement error can be expressed as the sum of systematic error (bias) and stochastic error [13]. Ideally, the bias should be zero and the stochastic component should be normally distributed with as little variance as possible.…”

Section: Measurement Performances Of Azure Kinectmentioning

confidence: 99%

Assessing the Accuracy of the Azure Kinect for Telerehabilitation After Breast Cancer Surgery

Cattaneo,

Fragasso,

Magni

et al. 2023

dHealth 2023

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Background: Rehabilitation plays a key role in the recovery of upper extremity function after breast cancer surgery. Motion capture (mocap) systems for serious gaming have shown the potential to enable home-based rehabilitation, but clinical accuracy needs to be examined. Objectives: Validation of markerless mocap systems for telerehabilitation after breast cancer surgical intervention. Methods: The accuracy of the markerless mocap device Azure Kinect in detecting compensatory movements and postural disturbances has been compared to a gold standard Optitrack system in five volunteers. Subsequently, a serious game for mocap-based shoulder exercises has been developed and integrated into a telerehabilitation platform. Results: The Azure Kinect shows good reliability for scapular elevation (ICC >0.80; MAE <2.1 cm) and trunk tilt (ICC=0.88; MAE=5°), moderate reliability for rounded shoulders (ICC=0.51; MAE=2.6cm) and poor reliability for kyphosis angle (ICC=0.22; MAE=18°). Conclusion: The Azure Kinect provides reasonable performance for shoulder rehabilitation. The proposed telerehabilitation platform has been tested by rehabilitation specialists and received positive feedback.

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“…This section summarizes the statistic properties of the systematic error, by using our previous work in [18]. Assuming radar also contains bias, the corresponding measurement is thus defined as:…”

Section: B Systematic Error and Sensor Biasmentioning

confidence: 99%

Joint bias estimation and localization in factor graph

Zhang

Malovetz

Gulati

et al. 2016

2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

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Abstract-This paper describes a new approach for cooperative localization by using both internal and external sensors. In contrast to the state-of-the-art methods, the proposed approach analyses the statistical properties of the systematic error during the transformation phase. A factor graph is formulated which jointly estimates both the biases and the locations. The proposed approach is evaluated by using simulated data from odometry, GPS and radar measurements. The experiment demonstrates excellent performance of the proposed approach in comparison to traditional techniques.

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Systematic Error Modeling and Bias Estimation

Cited by 7 publications

References 14 publications

Path Planning in Localization Uncertaining Environment Based on Dijkstra Method

Path Planning in Localization Uncertaining Environment Based on Dijkstra Method

Assessing the Accuracy of the Azure Kinect for Telerehabilitation After Breast Cancer Surgery

Joint bias estimation and localization in factor graph

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