Optimization based algorithm for correction of systematic odometry errors of mobile robot

Krivić, Senka; Mrzic, Aida; Velagić, Jasmin; Osmić, Nedim

doi:10.1109/ascc.2013.6606226

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…They have concentrated on different subfields of research. In [5], least squares calibration is used; estimation techniques such as back-propagation and neural networks in [6]; GPS data fusion with internal odometry data in [7,8]. …”

Section: Introductionmentioning

confidence: 99%

Improved Omnidirectional Odometry for a View-Based Mapping Approach

Valiente

Gil

Reinoso

et al. 2017

Sensors

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Abstract:This work presents an improved visual odometry using omnidirectional images. The main purpose is to generate a reliable prior input which enhances the SLAM (Simultaneous Localization and Mapping) estimation tasks within the framework of navigation in mobile robotics, in detriment of the internal odometry data. Generally, standard SLAM approaches extensively use data such as the main prior input to localize the robot. They also tend to consider sensory data acquired with GPSs, lasers or digital cameras, as the more commonly acknowledged to re-estimate the solution. Nonetheless, the modeling of the main prior is crucial, and sometimes especially challenging when it comes to non-systematic terms, such as those associated with the internal odometer, which ultimately turn to be considerably injurious and compromise the convergence of the system. This omnidirectional odometry relies on an adaptive feature point matching through the propagation of the current uncertainty of the system. Ultimately, it is fused as the main prior input in an EKF (Extended Kalman Filter) view-based SLAM system, together with the adaption of the epipolar constraint to the omnidirectional geometry. Several improvements have been added to the initial visual odometry proposal so as to produce better performance. We present real data experiments to test the validity of the proposal and to demonstrate its benefits, in contrast to the internal odometry. Furthermore, SLAM results are included to assess its robustness and accuracy when using the proposed prior omnidirectional odometry.

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Section: Introductionmentioning

confidence: 99%

Improved Omnidirectional Odometry for a View-Based Mapping Approach

Valiente

Gil

Reinoso

et al. 2017

Sensors

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“…Odometry is a method in which an encoder is attached to the robot's tires, and the amount of movement is determined by calculating the number of tire rotations [20][21][22][23][24]. Implementation is relatively simple and so odometry is used in many robots, but errors due to tire slip and sensor noise accumulate with movement, leading to significant error after extended travel.…”

Section: Odometrymentioning

confidence: 99%

Improving landmark detection accuracy for self-localization through baseboard recognition

Premachandra

Murakami

Gohara

et al. 2016

Int. J. Mach. Learn. & Cyber.

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Acquiring information related to the surrounding environment and environmental mapping are important issues for realizing autonomous robot movement in an unknown environment, and these processes require position estimations for self-localization. We have conducted previous research related to self-localization in indoor environments through baseboard recognition. In that method, a mobile robot performs self-localization using camera-acquired images, from which the intersection of baseboards and the vertical lines of attached doorframes are registered as landmarks. However, this method is limited in that self-localization cannot be performed in cases where baseboards are not partially available or where walls and baseboards are the same color. The present research aims at addressing these issues and implementing the method in an actual mobile robot. The implementing robot is allowed to move autonomously, performing mapping and self-localization in real time. Addressing the problem of same-colored walls requires identification of the border between the wall and the baseboard, so we propose an image processing method for identifying these lines. The problem of lack of baseboards is solved through supplemental use of odometry, which does not rely on visual characteristics. The proposed method is experimentally verified through use of our algorithm to recognize baseboards, perform self-localization, and create maps. The results verify that baseboards can be recognized even when they are the same color as walls. We furthermore verify that landmark-mapping performance when moving in hallways is improved over the previous version.

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“…The maximum likelihood estimation is applied by Renders [22] and other researchers to paratha meter identification of the manipulator kinematic geometric parameters, which achieved a good result. With the continuous improvement of the computing level, the Levenberg-Marquardt (LM) method [23], the extended Kalman filter method [24], the simulated annealing algorithm [25], the parameter optimization method, and other iterative algorithms are also applied to parameter identification of the complex system.…”

Section: Introductionmentioning

confidence: 99%

Calibration of a Manipulator With a Regularized Parameter Identification Method

Jiang

Luo

et al. 2022

IEEE Access

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As an inverse problem, the parameter identification of manipulators is essential to in-situ calibration. Since the ill-posed inverse kinematic model is sensitive to the measurement value, even tiny errors will make the geometric model of the manipulator wrongly identified. To overcome this problem, a Regularized Parameter Identification Method (RPIM) is proposed to calibrate the geometric model of the manipulator. The inverse kinematic model of a 6 DOF manipulator is modified by a Tikhonov regularization to overcome its ill-posed problem. The regularization parameter is optimized by the improved L curve method to adjust the initial model to a well-posed one that approximates the real situation. A calibration system is designed to evaluate the effectiveness of the suggested method. The position of the selected targets is tested by using a laser tracker. The experimental result shows that the absolute position errors of the manipulator are 2.533mm without calibration, 0.472mm by RPIM, 1.445mm by Least Square Method (LSM), 1.353mm by Gradient Descent (GD), and 0.956mm by Gauss-Newton (GN). It shows that the absolute position error of RPIM is reduced by 81.331% after calibration, which is superior to other methods.

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Optimization based algorithm for correction of systematic odometry errors of mobile robot

Cited by 3 publications

References 14 publications

Improved Omnidirectional Odometry for a View-Based Mapping Approach

Improved Omnidirectional Odometry for a View-Based Mapping Approach

Improving landmark detection accuracy for self-localization through baseboard recognition

Calibration of a Manipulator With a Regularized Parameter Identification Method

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