Dr. Joakim Rydell Profile

Dr. Joakim Rydell

at FOI-Swedish Defence Research Agency

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Author

Publications (8)

Proceedings Article | 31 May 2022 Paper

MONST3R: miniature optical navigation system through 3D recognition

Joakim Rydell, Erika Bilock

Proceedings Volume 12124, 1212405 (2022) https://doi.org/10.1117/12.2617717

KEYWORDS: 3D modeling, Cameras, Unmanned aerial vehicles, Optical filters, Onboard cameras, Navigation systems, Optical navigation

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Proceedings Article | 20 September 2020 Presentation + Paper

Laser sensing from small UAVs

Michael Tulldahl, Max Holmberg, Oskar Karlsson, Joakim Rydell, Erika Bilock, Linnéa Axelsson, Gustav Tolt, Jan Svedin

Proceedings Volume 11538, 115380C (2020) https://doi.org/10.1117/12.2575933

KEYWORDS: Unmanned aerial vehicles, LIDAR, Sensors, Active sensors, 3D metrology

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Proceedings Article | 10 October 2019 Presentation + Paper

Lidar-based positioning in forest environments

Michael Tulldahl, Joakim Rydell, Johan Holmgren, Jonas Nordlöf, Erik Willén

Proceedings Volume 11160, 1116005 (2019) https://doi.org/10.1117/12.2533299

KEYWORDS: LIDAR, Visualization, Cameras, Feature extraction, Sensors, Scanners, Navigation systems, Environmental sensing, Satellite navigation systems, Satellites

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Proceedings Article | 5 May 2017 Presentation + Paper

Three-dimensional hyperspectral imaging technique

Jörgen Ahlberg, Ingmar Renhorn, Tomas Chevalier, Joakim Rydell, David Bergström

Proceedings Volume 10198, 1019805 (2017) https://doi.org/10.1117/12.2262456

KEYWORDS: Staring arrays, Data acquisition, 3D image reconstruction, 3D modeling, Cameras, Sensors, Hyperspectral imaging, Optical filters, Linear filtering, Remote sensing, 3D image processing

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Proceedings Article | 15 October 2013 Paper

Geometric calibration of thermal cameras

Philip Engström, Håkan Larsson, Joakim Rydell

Proceedings Volume 8897, 88970C (2013) https://doi.org/10.1117/12.2030952

KEYWORDS: Calibration, Cameras, Aluminum, Visualization, Thermography, Distortion, Image visualization, Sensors, Reflectivity, Infrared cameras

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There exist several tools and methods for camera resectioning, i.e. geometric calibration for the purpose of estimating intrinsic and extrinsic parameters. The intrinsic parameters represent the internal properties of the camera such as focal length, principal point and distortion coefficients. The extrinsic parameters relate the cameras position to the world, i.e. how is the camera positioned and oriented in the world. With both sets of parameters known it is possible to relate a pixel in one camera to the world or to another camera. This is important in many applications, for example in stereo vision. The existing methods work well for standard visual cameras in most situations. Intrinsic parameters are usually estimated by imaging a well-defined pattern from different angles and distances. Checkerboard patterns are very often used for calibration since it is a well-defined pattern with easily detectable features. The intersections between the black and white squares form high contrast points which can be estimated with sub pixel accuracy. Knowing the precise dimension and structure of the pattern makes enables calculation of the intrinsic parameters. Extrinsic calibration can be performed in a similar manner if the exact position and orientation of the pattern is known. A common method is to distribute markers in the scene and to measure their exact locations. The key to good calibration is well-defined points and accurate measurements. Thermal cameras are a subset of infrared cameras that work with long wavelengths, usually between 9 and 14 microns. At these wavelengths all objects above absolute zero temperature emit radiation making it ideal for passive imaging in complete darkness and widely used in military applications. The issue that arises when trying to perform a geometric calibration of a thermal camera is that the checkerboard emits more or less the same amount of radiation in the black squares as in the white. In other words, the calibration board that is optimal for calibration of visual cameras might be completely useless for thermal cameras. A calibration board for thermal cameras should ideally be a checkerboard with high contrast in thermal wavelengths. (It is of course possible to use other sorts of objects or patterns but since most tools and software expect a checkerboard pattern this is by far the most straightforward solution.) Depending on the application it should also be more or less portable and work booth in indoor and outdoor scenarios. In this paper we present several years of experience with calibration of thermal cameras in various scenarios. Checkerboards with high contrast both for indoor and outdoor scenarios are presented as well as different markers suitable for extrinsic calibration.

Proceedings Article | 19 November 2012 Paper

Sensor fusion for improved indoor navigation

Erika Emilsson, Joakim Rydell

Proceedings Volume 8542, 85420M (2012) https://doi.org/10.1117/12.974584

KEYWORDS: Imaging systems, Cameras, Sensor fusion, Navigation systems, Sensors, Error analysis, Environmental sensing, System integration, Visualization, Gyroscopes

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Proceedings Article | 3 June 2011 Paper

Submap joining smoothing and mapping for camera-based indoor localization and mapping

J. Bjärkefur, A. Karlsson, C. Grönwall, J. Rydell

Proceedings Volume 8056, 805609 (2011) https://doi.org/10.1117/12.883945

KEYWORDS: Sensors, Cameras, Process modeling, Imaging systems, Error analysis, Safety, Lithium, Environmental sensing, Telecommunications, RF communications

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Proceedings Article | 13 May 2010 Paper

Threat detection and tracking using 3D FLASH ladar data

Christina Grönwall, Gustav Tolt, Daniel Karlsson, Hadi Esteki, Joakim Rydell, Ernest Armstrong, Jack Woods

Proceedings Volume 7696, 76960N (2010) https://doi.org/10.1117/12.849973

KEYWORDS: Target detection, Image segmentation, Sensors, 3D acquisition, LIDAR, 3D metrology, Head, Cameras, Data modeling, Imaging systems

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Showing 5 of 8 publications

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