Pose estimation of a mobile robot based on fusion of IMU data and vision data using an extended Kalman filter

dc.contributor.authorAlatise, Mary B.
dc.contributor.authorHancke, Gerhard P.
dc.date.accessioned2017-11-15T12:55:23Z
dc.date.available2017-11-15T12:55:23Z
dc.date.issued2017-09-21
dc.description.abstractUsing a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs).en_ZA
dc.description.departmentElectrical, Electronic and Computer Engineeringen_ZA
dc.description.librarianam2017en_ZA
dc.description.sponsorshipThe National Researcher Foundation grant funded by the South African government in collaboration with the University of Pretoria.en_ZA
dc.description.urihttp://www.mdpi.com/journal/sensorsen_ZA
dc.identifier.citationAlatise, M.B. & Hancke, G.P. 2017, 'Pose estimation of a mobile robot based on fusion of IMU data and vision data using an extended Kalman filter', Sensors, vol. 17, no. 10, art. 2164, pp. 1-22.en_ZA
dc.identifier.isbn10.3390/s17102164
dc.identifier.issn1424-8220 (onlne)
dc.identifier.urihttp://hdl.handle.net/2263/63182
dc.language.isoenen_ZA
dc.publisherMDPI Publishingen_ZA
dc.rights© 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).en_ZA
dc.subjectPose estimationen_ZA
dc.subjectMobile roboten_ZA
dc.subjectInertial sensorsen_ZA
dc.subjectVisionen_ZA
dc.subjectObjecten_ZA
dc.subjectSpeeded-up robust feature (SURF)en_ZA
dc.subjectRandom sample consensus (RANSAC)en_ZA
dc.subjectExtended Kalman filter (EKF)en_ZA
dc.subjectKalman filtersen_ZA
dc.subjectPosition and orientationsen_ZA
dc.subjectObject detection algorithmsen_ZA
dc.subjectAutonomous mobile roboten_ZA
dc.subjectRobotsen_ZA
dc.subjectObject detectionen_ZA
dc.subjectMean square erroren_ZA
dc.subjectIterative methodsen_ZA
dc.subjectInertial navigation systemsen_ZA
dc.subjectEstimationen_ZA
dc.subjectDegrees of freedom (mechanics)en_ZA
dc.subjectBandpass filtersen_ZA
dc.titlePose estimation of a mobile robot based on fusion of IMU data and vision data using an extended Kalman filteren_ZA
dc.typeArticleen_ZA

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Alatise_Pose_2017.pdf
Size:
4.34 MB
Format:
Adobe Portable Document Format
Description:
Article

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.75 KB
Format:
Item-specific license agreed upon to submission
Description: