Dr. Michael Tulldahl Profile

Dr. Michael Tulldahl

at Swedish Defence Research Agency

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Author

Publications (20)

Proceedings Article | 2 November 2022 Presentation + Paper

Semantic segmentation of point clouds from scanning lidars

Maria Axelsson, Max Holmberg, Michael Tulldahl

Proceedings Volume 12272, 1227206 (2022) https://doi.org/10.1117/12.2645181

KEYWORDS: LIDAR, Convolution, Sensors, Unmanned aerial vehicles, Machine learning, Defense and security

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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 | 10 May 2018 Presentation + Paper

Laser profiling for airborne target classification

Ove Steinvall, Michael Tulldahl, Folke Berglund, Lars Allard

Proceedings Volume 10636, 1063602 (2018) https://doi.org/10.1117/12.2303965

KEYWORDS: Profiling, Target recognition, Sensors, Signal to noise ratio, Pulsed laser operation, 3D modeling, Airborne laser technology, LIDAR, Radar, Missiles

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Proceedings Article | 5 October 2017 Paper

Experimental evaluation of penetration capabilities of a Geiger-mode APD array laser radar system

Per Jonsson, Michael Tulldahl, Julia Hedborg, Markus Henriksson, Lars Sjöqvist

Proceedings Volume 10434, 1043405 (2017) https://doi.org/10.1117/12.2278443

KEYWORDS: LIDAR, Sensors, Signal detection, 3D acquisition, Single photon detectors, Target detection, Laser systems engineering, Avalanche photodiodes, Target recognition, Pulsed laser operation

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Laser radar 3D imaging has the potential to improve target recognition in many scenarios. One case that is challenging for most optical sensors is to recognize targets hidden in vegetation or behind camouflage. The range resolution of timeof- flight 3D sensors allows segmentation of obscuration and target if the surfaces are separated far enough so that they can be resolved as two distances. Systems based on time-correlated single-photon counting (TCSPC) have the potential to resolve surfaces closer to each other compared to laser radar systems based on proportional mode detection technologies and is therefore especially interesting. Photon counting detection is commonly performed with Geigermode Avalanche Photodiodes (GmAPD) that have the disadvantage that they can only detect one photon per laser pulse per pixel. A strong return from an obscuring object may saturate the detector and thus limit the possibility to detect the hidden target even if photons from the target reach the detector. The operational range where good foliage penetration is observed is therefore relatively narrow for GmAPD systems. In this paper we investigate the penetration capability through semi-transparent surfaces for a laser radar with a 128×32 pixel GmAPD array and a 1542 nm wavelength laser operating at a pulse repetition frequency of 90 kHz. In the evaluation a screen was placed behind different canvases with varying transmissions and the detected signals from the surfaces for different laser intensities were measured. The maximum return from the second surface occurs when the total detection probability is around 0.65-0.75 per pulse. At higher laser excitation power the signal from the second surface decreases. To optimize the foliage penetration capability it is thus necessary to adaptively control the laser power to keep the returned signal within this region. In addition to the experimental results, simulations to study the influence of the pulse energy on penetration through foliage in a scene with targets behind vegetation are presented. The optimum detection of targets occurs here at a slightly higher total photon count rate probability because a number of pixel have no obscuration in front the target in their field of view.

SPIE Journal Paper | 21 October 2016

Laser range profiling for small target recognition

Ove Steinvall, Michael Tulldahl

OE, Vol. 56, Issue 03, 031206, (October 2016) https://doi.org/10.1117/12.10.1117/1.OE.56.3.031206

KEYWORDS: Profiling, Target recognition, Unmanned aerial vehicles, Sensors, Optical simulations, Target detection, Optical engineering, Solid modeling, Pulsed laser operation, LIDAR

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Long range identification (ID) or ID at closer range of small targets has its limitations in imaging due to the demand for very high-transverse sensor resolution. This is, therefore, a motivation to look for one-dimensional laser techniques for target ID. These include laser vibrometry and laser range profiling. Laser vibrometry can give good results, but is not always robust as it is sensitive to certain vibrating parts on the target being in the field of view. Laser range profiling is attractive because the maximum range can be substantial, especially for a small laser beam width. A range profiler can also be used in a scanning mode to detect targets within a certain sector. The same laser can also be used for active imaging when the target comes closer and is angularly resolved. Our laser range profiler is based on a laser with a pulse width of 6 ns (full width half maximum). This paper will show both experimental and simulated results for laser range profiling of small boats out to a 6 to 7-km range and a unmanned arrial vehicle (UAV) mockup at close range (1.3 km). The naval experiments took place in the Baltic Sea using many other active and passive electro-optical sensors in addition to the profiling system. The UAV experiments showed the need for a high-range resolution, thus we used a photon counting system in addition to the more conventional profiler used in the naval experiments. This paper shows the influence of target pose and range resolution on the capability of classification. The typical resolution (in our case 0.7 m) obtainable with a conventional range finder type of sensor can be used for large target classification with a depth structure over 5 to 10 m or more, but for smaller targets such as a UAV a high resolution (in our case 7.5 mm) is needed to reveal depth structures and surface shapes. This paper also shows the need for 3-D target information to build libraries for comparison of measured and simulated range profiles. At closer ranges, full 3-D images should be preferable.

Proceedings Article | 13 May 2016 Paper

Application and capabilities of lidar from small UAV

Michael Tulldahl, Håkan Larsson, Gustav Tolt, Fredrik Bissmarck, Christina Grönwall, Jonas Nordlöf

Proceedings Volume 9832, 98320V (2016) https://doi.org/10.1117/12.2224258

KEYWORDS: LIDAR, Unmanned aerial vehicles, Clouds, 3D acquisition, 3D image processing, Target detection, Target recognition, Roads, 3D metrology, Vegetation

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Proceedings Article | 13 May 2016 Paper

Laser range profiling for small target recognition

Ove Steinvall, Michael Tulldahl

Proceedings Volume 9832, 98320E (2016) https://doi.org/10.1117/12.2223754

KEYWORDS: Profiling, Target recognition, Target detection, Imaging systems, Sensors, Unmanned aerial vehicles, Cameras, Solid modeling, Optical simulations, Computer aided design

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Proceedings Article | 16 October 2015 Paper

3D sensing and imaging for UAVs

Christina Grönwall, Gustav Tolt, Patrik Lif, Håkan Larsson, Fredrik Bissmarck, Michael Tulldahl, Markus Henriksson, Per Wikberg, Mirko Thorstensson

Proceedings Volume 9649, 96490C (2015) https://doi.org/10.1117/12.2192834

KEYWORDS: Unmanned aerial vehicles, Sensors, LIDAR, 3D modeling, 3D acquisition, Visualization, Target detection, Clouds, Signal processing, Data modeling

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Proceedings Article | 16 October 2015 Paper

Accuracy evaluation of 3D lidar data from small UAV

H. Tulldahl, Fredrik Bissmarck, Håkan Larsson, Christina Grönwall, Gustav Tolt

Proceedings Volume 9649, 964903 (2015) https://doi.org/10.1117/12.2194508

KEYWORDS: LIDAR, Calibration, Unmanned aerial vehicles, Clouds, Sensors, Microelectromechanical systems, Data processing, Neodymium, Ions, Data modeling

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Proceedings Article | 13 October 2014 Paper

Processing of airborne lidar bathymetry data for detailed sea floor mapping

H. Michael Tulldahl

Proceedings Volume 9250, 92500E (2014) https://doi.org/10.1117/12.2068940

KEYWORDS: LIDAR, Vegetation, Data modeling, Water, Associative arrays, Target detection, Video, Backscatter, Monte Carlo methods, Scattering

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Proceedings Article | 13 October 2014 Paper

Lidar on small UAV for 3D mapping

H. Michael Tulldahl, Håkan Larsson

Proceedings Volume 9250, 925009 (2014) https://doi.org/10.1117/12.2068448

KEYWORDS: LIDAR, Calibration, Unmanned aerial vehicles, Clouds, Sensors, Reflectivity, Laser scanners, Data processing, 3D acquisition, Scanners

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Small UAV:s (Unmanned Aerial Vehicles) are currently in an explosive technical development phase. The performance of UAV-system components such as inertial navigation sensors, propulsion, control processors and algorithms are gradually improving. Simultaneously, lidar technologies are continuously developing in terms of reliability, accuracy, as well as speed of data collection, storage and processing. The lidar development towards miniature systems with high data rates has, together with recent UAV development, a great potential for new three dimensional (3D) mapping capabilities. Compared to lidar mapping from manned full-size aircraft a small unmanned aircraft can be cost efficient over small areas and more flexible for deployment. An advantage with high resolution lidar compared to 3D mapping from passive (multi angle) photogrammetry is the ability to penetrate through vegetation and detect partially obscured targets. Another advantage is the ability to obtain 3D data over the whole survey area, without the limited performance of passive photogrammetry in low contrast areas. The purpose of our work is to demonstrate 3D lidar mapping capability from a small multirotor UAV. We present the first experimental results and the mechanical and electrical integration of the Velodyne HDL-32E lidar on a six-rotor aircraft with a total weight of 7 kg. The rotating lidar is mounted at an angle of 20 degrees from the horizontal plane giving a vertical field-of-view of 10-50 degrees below the horizon in the aircraft forward directions. For absolute positioning of the 3D data, accurate positioning and orientation of the lidar sensor is of high importance. We evaluate the lidar data position accuracy both based on inertial navigation system (INS) data, and on INS data combined with lidar data. The INS sensors consist of accelerometers, gyroscopes, GPS, magnetometers, and a pressure sensor for altimetry. The lidar range resolution and accuracy is documented as well as the capability for target surface reflectivity estimation based on measurements on calibration standards. Initial results of the general mapping capability including the detection through partly obscured environments is demonstrated through field data collection and analysis.

Proceedings Article | 3 June 2013 Paper

Sea floor classification with satellite data and airborne lidar bathymetry

H. Michael Tulldahl, Petra Philipson, Hans Kautsky, Sofia Wikström

Proceedings Volume 8724, 87240B (2013) https://doi.org/10.1117/12.2015727

KEYWORDS: LIDAR, Satellites, Vegetation, Data modeling, Data corrections, Image classification, Reflectivity, Accuracy assessment, Earth observing sensors, Sun

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Proceedings Article | 14 May 2012 Paper

Feasibility study for airborne fluorescence/reflectivity lidar bathymetry

Ove Steinvall, Hans Kautsky, Michael Tulldahl, Erika Wollner

Proceedings Volume 8379, 837914 (2012) https://doi.org/10.1117/12.919137

KEYWORDS: Luminescence, Reflectivity, LIDAR, Vegetation, Receivers, Raman spectroscopy, Sensors, Laser induced fluorescence, Backscatter, Signal to noise ratio

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There is a demand from the authorities to have good maps of the coastal environment for their exploitation and preservation of the coastal areas. The goal for environmental mapping and monitoring is to differentiate between vegetation and non-vegetated bottoms and, if possible, to differentiate between species. Airborne lidar bathymetry is an interesting method for mapping shallow underwater habitats. In general, the maximum depth range for airborne laser exceeds the possible depth range for passive sensors. Today, operational lidar systems are able to capture the bottom (or vegetation) topography as well as estimations of the bottom reflectivity using e.g. reflected bottom pulse power. In this paper we study the possibilities and advantages for environmental mapping, if laser sensing would be further developed from single wavelength depth sounding systems to include multiple emission wavelengths and fluorescence receiver channels. Our results show that an airborne fluorescence lidar has several interesting features which might be useful in mapping underwater habitats. An example is the laser induced fluorescence giving rise to the emission spectrum which could be used for classification together with the elastic lidar signal. In the first part of our study, vegetation and substrate samples were collected and their spectral reflectance and fluorescence were subsequently measured in laboratory. A laser wavelength of 532 nm was used for excitation of the samples. The choice of 532 nm as excitation wavelength is motivated by the fact that this wavelength is commonly used in bathymetric laser scanners and that the excitation wavelengths are limited to the visual region as e.g. ultraviolet radiation is highly attenuated in water. The second part of our work consisted of theoretical performance calculations for a potential real system, and comparison of separability between species and substrate signatures using selected wavelength regions for fluorescence sensing.

Proceedings Article | 24 October 2011 Paper

An overview of the airborne bathymetric lidar reflectance data processing

Xin Liu, H. Michael Tulldahl, Andreas Axelsson

Proceedings Volume 8286, 828608 (2011) https://doi.org/10.1117/12.912742

KEYWORDS: Reflectivity, LIDAR, Image processing, Image classification, Acoustics, Data processing, Ocean optics, Orthophoto maps, Remote sensing, Infrared radiation

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Proceedings Article | 7 November 2007 Paper

Lidar for shallow underwater target detection

H. Michael Tulldahl, Magnus Pettersson

Proceedings Volume 6739, 673906 (2007) https://doi.org/10.1117/12.737872

KEYWORDS: LIDAR, Signal to noise ratio, Sensors, Target detection, Ocean optics, Receivers, Backscatter, Signal attenuation, Water, Pulsed laser operation

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Proceedings Article | 3 October 2007 Paper

Sea floor classification from airborne lidar data

H. Michael Tulldahl, Claes Vahlberg, Andreas Axelsson, Henrik Karlsson, Peter Jonsson

Proceedings Volume 6750, 675003 (2007) https://doi.org/10.1117/12.737922

KEYWORDS: LIDAR, Video, Data modeling, Reflectivity, Vegetation, Signal attenuation, Calibration, Eye, Ocean optics, Receivers

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Proceedings Article | 14 May 2007 Paper

Overview of range gated imaging at FOI

Ove Steinvall, Pierre Andersson, Magnus Elmqvist, Michael Tulldahl

Proceedings Volume 6542, 654216 (2007) https://doi.org/10.1117/12.719191

KEYWORDS: Gated imaging, Imaging systems, Target detection, Sensors, LIDAR, Cameras, Reflectivity, Target recognition, Thermography, 3D image reconstruction

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Proceedings Article | 5 October 2006 Paper

Experimental evaluation of underwater range-gated viewing in natural waters

H. M. Tulldahl, P. Andersson, A. Olsson, A. Andersson

Proceedings Volume 6395, 639506 (2006) https://doi.org/10.1117/12.689860

KEYWORDS: Modulation transfer functions, Cameras, Imaging systems, Spatial frequencies, Video, Target detection, Receivers, Image resolution, Lamps, Backscatter

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Proceedings Article | 22 September 2003 Paper

Underwater irradiance fluctuations of a laser beam after transmission through a wavy sea surface

H. Michael Tulldahl

Proceedings Volume 5149, (2003) https://doi.org/10.1117/12.519769

KEYWORDS: Cameras, Calibration, Video, Data modeling, Signal attenuation, Systems modeling, Error analysis, Distortion, Spatial resolution, Optical testing

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