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Fusing Visual and Inertial Information
KTH, School of Electrical Engineering (EES), Signal Processing.
2011 (English)Licentiate thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology , 2011. , vii, 21 p.
Series
Trita-EE, ISSN 1653-5146 ; 2011:023
Keyword [en]
sensor fusion, inertial measurement unit, monocular camera, inertial navigation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-32112ISBN: 978-91-7415-919-6OAI: oai:DiVA.org:kth-32112DiVA: diva2:409014
Presentation
2011-04-05, Q2, KTH Royal Institute of Technology, Stockholm, 13:15 (English)
Opponent
Supervisors
Note
QC 20110412Available from: 2011-04-12 Created: 2011-04-06 Last updated: 2011-04-12Bibliographically approved
List of papers
1. Joint calibration of an inertial measurement unit and coordinate transformation parameters using a monocular camera
Open this publication in new window or tab >>Joint calibration of an inertial measurement unit and coordinate transformation parameters using a monocular camera
2010 (English)In: 2010 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2010, IEEE conference proceedings, 2010, 1-7 p.Conference paper (Refereed)
Abstract [en]

An estimation procedure for calibration of a low-cost inertial measurement unit (IMU), using a rigidly mounted monocular camera, is presented. The parameters of a sensor model that captures misalignments, scale and offset errors are estimated jointly with the IMU-camera coordinate transformation parameters using a recursive Sigma-Point Kalman Filter. The method requires only a simple visual calibration pattern. A simulation study indicates the filter's ability to reach subcentimeter and subdegree accuracy.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2010
Keyword
Calibration patterns, Co-ordinate transformation, Estimation procedures, Inertial measurement unit, Joint calibration, Monocular cameras, Offset errors, Sensor model, Sigma-point Kalman filters, Simulation studies, Cameras, Navigation, Sensors, Units of measurement, Calibration
National Category
Signal Processing Control Engineering
Identifiers
urn:nbn:se:kth:diva-32314 (URN)10.1109/IPIN.2010.5646840 (DOI)000345209100030 ()2-s2.0-78650733532 (ScopusID)9781424458646 (ISBN)
Conference
2010 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2010. Zurich. 15 September 2010 - 17 September 2010
Note

QC 20110412

Available from: 2012-10-05 Created: 2011-04-12 Last updated: 2015-06-10Bibliographically approved
2. Camera-aided inertial navigation using epipolar points
Open this publication in new window or tab >>Camera-aided inertial navigation using epipolar points
2010 (English)In: 2010 IEEE-ION POSITION LOCATION AND NAVIGATION SYMPOSIUM PLANS, New York: IEEE conference proceedings, 2010, 303-309 p.Conference paper (Refereed)
Abstract [en]

Due to the rapid error growth of navigation systems using low-cost inertial measurement units there is a need to fuse the information with complementary sensors. In this paper a monocular camera is used to aid the system. Unlike SLAM-like approaches the problem of estimating the location of each feature point viewed in a scene is avoided, instead estimated epipolar points on the image plane are used. By maintaining a buffer of past views, the method mimics a short-term visual memory which imposes multiple constraints on the estimation problem. The result is a Sigma-Point Kalman filter in square-root form with a linear and efficient time-update. A simulation study is presented indicating the filter's capacity to constrain the rate of error growth of an inertial navigation system. The filter may also find useful applications when fusing with additional sensors.

Place, publisher, year, edition, pages
New York: IEEE conference proceedings, 2010
Series
, IEEE-ION Position Location and Navigation Symposium, ISSN 2153-358X
Keyword
Inertial navigation, monocular camera, sensor fusion, Sigma-Point Kalman filter
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-32110 (URN)10.1109/PLANS.2010.5507246 (DOI)000287515800084 ()2-s2.0-77955022164 (ScopusID)978-1-4244-5037-4 (ISBN)
Conference
Position Location and Navigation Symposium (PLANS). Palm Springs, CA. MAY 04-06, 2010
Note

QC 20110407

Available from: 2012-11-13 Created: 2011-04-06 Last updated: 2012-11-13Bibliographically approved
3. Self-motion and wind velocity estimation for small-scale UAVs
Open this publication in new window or tab >>Self-motion and wind velocity estimation for small-scale UAVs
2011 (English)In: 2011 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2011, 1166-1171 p.Conference paper (Refereed)
Abstract [en]

For small-scale Unmanned Aerial Vehicles (UAV) to operate indoor, in urban canyons or other scenarios where signals from global navigation satellite systems are denied or impaired, alternative estimation and control strategies must be applied. In this paper a system is proposed that estimates the self-motion and wind velocity by fusing information from airspeed sensors, an inertial measurement unit (IMU) and a monocular camera. Such estimates can be used in control systems for managing wind disturbances or chemical plume based tracking strategies. Simulation results indicate that while the inertial dead-reckoning process is subject to drift, the system is capable of separating the self-motion and wind velocity from the airspeed information.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2011
Keyword
Cameras, Covariance matrix, Estimation, Feature extraction, Noise, Sensors, Visualization
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-32325 (URN)10.1109/ICRA.2011.5979676 (DOI)000324383400052 ()2-s2.0-84871683833 (ScopusID)978-1-61284-386-5 (ISBN)
Conference
IEEE International Conference on Robotics and Automation (ICRA). Shanghai, China. May 9-13 2011
Funder
ICT - The Next Generation
Note

QC 20110412

Available from: 2012-11-13 Created: 2011-04-12 Last updated: 2014-09-30Bibliographically approved
4. Fusing visual tags and inertial information for indoor navigation
Open this publication in new window or tab >>Fusing visual tags and inertial information for indoor navigation
2012 (English)In: 2012 IEEE/ION Position Location And Navigation Symposium (PLANS), IEEE , 2012, 535-540 p.Conference paper (Refereed)
Abstract [en]

We present a navigation system based on a monocular camera and an inertial measurement unit. The system detects visual tags and fuses the measurements on the image plane with inertial signals to perform pose estimation and localization using a Sigma-Point Kalman filter. The tags are detected by edge-based feature extraction and channel codes. During periods in which tags are not visible, epipolar constraints, arising from past views, are exploited to significantly reduce the position error growth rate. The experimental results in an office building indicate capabilities for indoor navigation.

Place, publisher, year, edition, pages
IEEE, 2012
Series
, IEEE - ION Position Location and Navigation Symposium, ISSN 2153-358X
Keyword
visual tags, epipoles, sensor fusion, inertial navigation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-32326 (URN)10.1109/PLANS.2012.6236924 (DOI)000309273900068 ()2-s2.0-84866258772 (ScopusID)978-146730386-6 (ISBN)
Conference
2012 IEEE/ION Position, Location and Navigation Symposium, PLANS 2012; Myrtle Beach, SC; 23 April 2012 through 26 April 2012
Funder
ICT - The Next Generation
Note

QC 20110412. Updated from manuscript to conference paper.

Available from: 2011-04-12 Created: 2011-04-12 Last updated: 2013-04-15Bibliographically approved

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