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Törnqvist, David
Publications (10 of 56) Show all publications
Ovrén, H., Forssén, P.-E. & Törnqvist, D. (2015). Improving RGB-D Scene Reconstruction using Rolling Shutter Rectification. In: Yu Sun, Aman Behal & Chi-Kit Ronald Chung (Ed.), New Development in Robot Vision: (pp. 55-71). Springer Berlin/Heidelberg
Open this publication in new window or tab >>Improving RGB-D Scene Reconstruction using Rolling Shutter Rectification
2015 (English)In: New Development in Robot Vision / [ed] Yu Sun, Aman Behal & Chi-Kit Ronald Chung, Springer Berlin/Heidelberg, 2015, p. 55-71Chapter in book (Refereed)
Abstract [en]

Scene reconstruction, i.e. the process of creating a 3D representation (mesh) of some real world scene, has recently become easier with the advent of cheap RGB-D sensors (e.g. the Microsoft Kinect).

Many such sensors use rolling shutter cameras, which produce geometrically distorted images when they are moving. To mitigate these rolling shutter distortions we propose a method that uses an attached gyroscope to rectify the depth scans.We also present a simple scheme to calibrate the relative pose and time synchronization between the gyro and a rolling shutter RGB-D sensor.

For scene reconstruction we use the Kinect Fusion algorithm to produce meshes. We create meshes from both raw and rectified depth scans, and these are then compared to a ground truth mesh. The types of motion we investigate are: pan, tilt and wobble (shaking) motions.

As our method relies on gyroscope readings, the amount of computations required is negligible compared to the cost of running Kinect Fusion.

This chapter is an extension of a paper at the IEEE Workshop on Robot Vision [10]. Compared to that paper, we have improved the rectification to also correct for lens distortion, and use a coarse-to-fine search to find the time shift more quicky.We have extended our experiments to also investigate the effects of lens distortion, and to use more accurate ground truth. The experiments demonstrate that correction of rolling shutter effects yields a larger improvement of the 3D model than correction for lens distortion.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2015
Series
Cognitive Systems Monographs, ISSN 1867-4925 ; 23
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-114344 (URN)10.1007/978-3-662-43859-6_4 (DOI)978-3-662-43858-9 (ISBN)978-3-662-43859-6 (ISBN)
Projects
Learnable Camera Motion Models
Available from: 2015-02-19 Created: 2015-02-19 Last updated: 2018-06-19Bibliographically approved
Grönwall, C., Törnqvist, D., Larsson, H. & Engström, P. (2013). Concurrent object recognition and localization for first responder applications. In: : . Paper presented at TAMSEC.
Open this publication in new window or tab >>Concurrent object recognition and localization for first responder applications
2013 (English)Conference paper, Poster (with or without abstract) (Other academic)
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-128050 (URN)
Conference
TAMSEC
Available from: 2016-05-16 Created: 2016-05-16 Last updated: 2016-08-31
Callmer, J., Törnqvist, D. & Gustafsson, F. (2013). Robust Heading Estimation Indoors. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Robust Heading Estimation Indoors
2013 (English)Report (Other academic)
Abstract [en]

Indoor positioning in unknown environments is crucial for rescue personnel and future infotainment systems. Dead-reckoning inertial sensor data gives accurate estimate of distance, for instance using zero velocity updates, while the heading estimation problem is inherently more difficult due to the large degree of magnetic disturbances indoors. We propose a Kalman filter bank approach based on supporting a magnetic compass with gyroscope turn rate information, where a hidden Markov model is used to model the presence of magnetic disturbances. In parallel, we suggest to run a robust heading estimation system based on data from a sliding window. The robust estimate is used to detect filter divergence, and to restart the filter when needed. The underlying assumptions and the heading estimation performance are supported in field trials using more than 500 data sets from more than 50 venues in 5 continents.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. p. 13
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3061
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-91393 (URN)
Funder
eLLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsLinnaeus research environment CADICS
Available from: 2013-04-23 Created: 2013-04-23 Last updated: 2013-05-08
Callmer, J., Törnqvist, D. & Gustafsson, F. (2013). Robust Heading Estimation Indoors using Convex Optimization. In: 2013 16TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION): . Paper presented at 16th International Conference on Information Fusion (FUSION) (pp. 1173-1179). IEEE
Open this publication in new window or tab >>Robust Heading Estimation Indoors using Convex Optimization
2013 (English)In: 2013 16TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), IEEE , 2013, p. 1173-1179Conference paper, Published paper (Refereed)
Abstract [en]

The problem of estimating heading is central in the indoor positioning problem based on measurements from inertial measurement and magnetic units, Integrating rate of turn angular rate gives the heading with unknown initial condition and a linear drift over time, while the magnetometer gives absolute heading, but m here long segments of data are useless in practice because of magnetic disturbances. A basic Kalman filter approach with outlier rejection has turned out to be difficult to use with high integrity. Here, we propose an approach based on convex optimization, where segments of good magnetometer data are separated from disturbed data and jointly fused with the yaw rate measurements. The optimization framework is flexible with many degrees of freedom in the modeling phase, and we outline one design. A recursive solution to the optimization is derived, which has a computational complexity comparable to the simplest possible Kalman filter. The performance is evaluated using data from a handheld smartphone for a large amount of indoor trajectories, and the result demonstrates that the method effectively resolves the magnetic disturbances.

Place, publisher, year, edition, pages
IEEE, 2013
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-111318 (URN)000341370000157 ()978-605-86311-1-3 (ISBN)
Conference
16th International Conference on Information Fusion (FUSION)
Available from: 2014-10-14 Created: 2014-10-14 Last updated: 2014-10-14
Callmer, J., Törnqvist, D. & Gustafsson, F. (2013). Robust Heading Estimation Indoors using Convex Optimization. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Robust Heading Estimation Indoors using Convex Optimization
2013 (English)Report (Other academic)
Abstract [en]

The problem of estimating heading is central in the indoor positioning problem based on mea- surements from inertial measurement and magnetic units. Integrating rate of turn angular rate gives the heading with unknown initial condition and a linear drift over time, while the magnetometer gives absolute heading, but where long segments of data are useless in prac- tice because of magnetic disturbances. A basic Kalman filter approach with outlier rejection has turned out to be difficult to use with high integrity. Here, we propose an approach based on convex optimization, where segments of good magnetometer data are separated from disturbed data and jointly fused with the yaw rate measurements. The optimization framework is flexible with many degrees of freedom in the modeling phase, and we outline one design. A recursive solution to the optimization is derived, which has a computational complexity comparable to the simplest possible Kalman filter. The performance is evaluated using data from a handheld smartphone for a large amount of indoor trajectories, and the result demonstrates that the method effectively resolves the magnetic disturbances.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. p. 9
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3060
Keywords
Heading estimation, magnetometer, gyro, disturbances, optimization
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-91392 (URN)LiTH-ISY-R-3060 (ISRN)
Funder
eLLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsLinnaeus research environment CADICS
Available from: 2013-04-23 Created: 2013-04-23 Last updated: 2014-06-16Bibliographically approved
Ovrén, H., Forssén, P.-E. & Törnqvist, D. (2013). Why Would I Want a Gyroscope on my RGB-D Sensor?. In: : . Paper presented at IEEE Workshop on Robot Vision 2013, Clearwater Beach, Florida, USA, January 16-17, 2013 (pp. 68-75). IEEE
Open this publication in new window or tab >>Why Would I Want a Gyroscope on my RGB-D Sensor?
2013 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

Many RGB-D sensors, e.g. the Microsoft Kinect, use rolling shutter cameras. Such cameras produce geometrically distorted images when the sensor is moving. To mitigate these rolling shutter distortions we propose a method that uses an attached gyroscope to rectify the depth scans. We also present a simple scheme to calibrate the relative pose and time synchronization between the gyro and a rolling shutter RGB-D sensor. We examine the effectiveness of our rectification scheme by coupling it with the the Kinect Fusion algorithm. By comparing Kinect Fusion models obtained from raw sensor scans and from rectified scans, we demonstrate improvement for three classes of sensor motion: panning motions causes slant distortions, and tilt motions cause vertically elongated or compressed objects. For wobble we also observe a loss of detail, compared to the reconstruction using rectified depth scans. As our method relies on gyroscope readings, the amount of computations required is negligible compared to the cost of running Kinect Fusion.

Place, publisher, year, edition, pages
IEEE, 2013
Keywords
RGB-D sensor, rolling shutter, Kinect Fusion, Kinect, calibration
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-87751 (URN)10.1109/WORV.2013.6521916 (DOI)978-1-4673-5647-3 (ISBN)978-1-4673-5646-6 (ISBN)
Conference
IEEE Workshop on Robot Vision 2013, Clearwater Beach, Florida, USA, January 16-17, 2013
Projects
Embodied Visual Object Recognition
Funder
Swedish Research Council, Embodied Visual Object Recognition
Available from: 2013-02-08 Created: 2013-01-22 Last updated: 2015-12-10Bibliographically approved
Skoglar, P., Orguner, U., Törnqvist, D. & Gustafsson, F. (2012). Pedestrian Tracking with an Infrared Sensor using Road Network Information. EURASIP Journal on Advances in Signal Processing, 1(26), 2012a
Open this publication in new window or tab >>Pedestrian Tracking with an Infrared Sensor using Road Network Information
2012 (English)In: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 1, no 26, p. 2012a-Article in journal (Refereed) Published
Abstract [en]

This article presents a pedestrian tracking methodology using an infrared sensor for surveillance applications. A distinctive feature of this study compared to the existing pedestrian tracking approaches is that the road network information is utilized for performance enhancement. A multiple model particle filter, which uses two different motion models, is designed for enabling the tracking of both road-constrained (on-road) and unconstrained (off-road) targets. The lateral position of the pedestrians on the walkways are taken into account by a specific on-road target model. The overall framework seamlessly integrates the negative information of occlusion events into the algorithm for which the required modifications are discussed. The resulting algorithm is illustrated on real data from a field trial for different scenarios.

Place, publisher, year, edition, pages
Springer, 2012
Keywords
Pedestrian tracking, Infrared sensor, Road network, Particle filter, Multiple model, Occlusion
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-76888 (URN)10.1186/1687-6180-2012-26 (DOI)
Projects
CADICSSecurity LinkExtended Target Tracking
Funder
Swedish Research CouncilSecurity Link
Available from: 2012-04-23 Created: 2012-04-23 Last updated: 2017-12-07
Skoglar, P., Orguner, U., Törnqvist, D. & Gustafsson, F. (2012). Road Target Search and Tracking with Gimballed Vision Sensor on a UAV. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Road Target Search and Tracking with Gimballed Vision Sensor on a UAV
2012 (English)Report (Other academic)
Abstract [en]

This work considers a sensor management problem where a number of road bounded vehicles are monitored by a UAV with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new targets by controlling the pointing direction of the vision sensor and the motion of the UAV. A planner based on a state-machine is proposed with three different modes; target tracking, known target search, and new target search. A high-level decision maker chooses among these sub-tasks to obtain an overall situational awareness. A utility measure for evaluating the combined search and target tracking performance is also proposed. By using this measure it is possible to evaluate and compare the rewards of updating known targets versus searching for new targets in the same framework. The targets are assumed to be road bounded and the road network information is used both to improve the tracking and sensor management performance. The tracking and search are based on flexible target density representations provided by particle mixtures and deterministic grids.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. p. 33
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3049
Keywords
UAV surveillance, Sensor management, Path planning, Search theory, Road target tracking, Particle filter, Stochastic scheduling, Security and monitoring
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-76891 (URN)LiTH-ISY-R-3049 (ISRN)
Projects
CADICSSecurity LinkExtended Target Tracking
Funder
Swedish Research Council
Available from: 2012-04-23 Created: 2012-04-23 Last updated: 2014-09-19
Skoglar, P., Orguner, U., Törnqvist, D. & Gustafsson, F. (2012). Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle. Remote Sensing, 4(7), 2076-2111
Open this publication in new window or tab >>Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle
2012 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 4, no 7, p. 2076-2111Article in journal (Refereed) Published
Abstract [en]

This article considers a sensor management problem where a number of road bounded vehicles are monitored by an unmanned aerial vehicle (UAV) with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new targets by controlling the pointing direction of the vision sensor and the motion of the UAV. A planner based on a state-machine is proposed with three different modes; target tracking, known target search, and new target search. A high-level decision maker chooses among these sub-tasks to obtain an overall situational awareness. A utility measure for evaluating the combined search and target tracking performance is also proposed. By using this measure it is possible to evaluate and compare the rewards of updating known targets versus searching for new targets in the same framework. The targets are assumed to be road bounded and the road network information is used both to improve the tracking and sensor management performance. The tracking and search are based on flexible target density representations provided by particle mixtures and deterministic grids.

Keywords
UAV surveillance, Sensor management, Path planning, Search theory, Road target tracking, Particle filter, Stochastic scheduling, Security and monitoring
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-80198 (URN)10.3390/rs4072076 (DOI)000306759700011 ()
Projects
CADICSSecurity LinkExtended Target Tracking
Funder
Swedish Research Council
Available from: 2012-08-22 Created: 2012-08-22 Last updated: 2017-12-07Bibliographically approved
Skoglar, P. & Törnqvist, D. (2012). Simultaneous Camera Orientation Estimation and Road Target Tracking. In: Proceedings of the 15th International Conference on Information Fusion: . Paper presented at International Conference on Information Fusion, Singapore, July 9-12, 2012 (pp. 802-807). IEEE
Open this publication in new window or tab >>Simultaneous Camera Orientation Estimation and Road Target Tracking
2012 (English)In: Proceedings of the 15th International Conference on Information Fusion, IEEE , 2012, p. 802-807Conference paper, Published paper (Refereed)
Abstract [en]

Airborne surveillance systems equipped with a vision/infrared camera require good knowledge about the position and orientation of the camera for successful tracking of ground targets. In particular, this is essential when incorporating prior information, like road maps, that is expressed relative a global reference system. Usually, it is possible to obtain good positioning with inertial/satellite navigation systems, but estimating the orientation is generally more difficult. It might be possible to use SLAM (Simultaneous Localization and Mapping) or image registration approaches to support the navigation system, but not always since such approaches require stable features in the images. In this paper the problem of simultaneous orientation error estimation and road target tracking is considered by assuming that the target is constrained to a known road network. A particle filter approach is proposed and it is shown that the result of this filter is close to the performance of the ideal case where the orientation error is perfectly known. However, the performance depends on how informative the road path is and in rare cases the orientation error is unobservable.

Place, publisher, year, edition, pages
IEEE, 2012
Keywords
Camera orientation, Estimation, Road target tracking
National Category
Signal Processing Control Engineering
Identifiers
urn:nbn:se:liu:diva-85480 (URN)978-0-9824438-4-2 (ISBN)978-1-4673-0417-7 (ISBN)978-0-9824438-5-9 (ISBN)
Conference
International Conference on Information Fusion, Singapore, July 9-12, 2012
Funder
Security LinkSwedish Research Council
Available from: 2012-11-20 Created: 2012-11-20 Last updated: 2013-12-03
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