Mr. Björn Borgmann Profile

Björn Borgmann

at Fraunhofer IOSB

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Publications (5)

Proceedings Article | 12 September 2021 Presentation + Paper

LiDAR-based localization and automatic control of UAVs for mobile remote reconnaissance

Marcus Hammer, Björn Borgmann, Marcus Hebel, Michael Arens

Proceedings Volume 11866, 118660T (2021) https://doi.org/10.1117/12.2597192

KEYWORDS: Unmanned aerial vehicles, Sensors, LIDAR, Reconnaissance, Clouds, Environmental sensing, Automatic control, 3D modeling, Control systems

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Sensor-based monitoring of the surroundings of civilian vehicles is primarily relevant for driver assistance in road traffic, whereas in military vehicles, far-reaching reconnaissance of the environment is crucial for accomplishing the respective mission. Modern military vehicles are typically equipped with electro-optical sensor systems for such observation or surveillance purposes. However, especially when the line-of-sight to the onward route is obscured or visibility conditions are generally limited, more enhanced methods for reconnaissance are needed. The obvious benefit of micro-drones (UAVs) for remote reconnaissance is well known. The spatial mobility of UAVs can provide additional information that cannot be obtained on the vehicle itself. For example, the UAV could keep a fixed position in front and above the vehicle to gather information about the area ahead, or it could fly above or around obstacles to clear hidden areas. In a military context, this is usually referred to as manned-unmanned teaming (MUM-T). In this paper, we propose the use of vehicle-based electro-optical sensors as an alternative way for automatic control of (cooperative) UAVs in the vehicle’s vicinity. In its most automated form, the external control of the UAV only requires a 3D nominal position relative to the vehicle or in absolute geocoordinates. The flight path there and the maintaining of this position including obstacle avoidance are automatically calculated on-board the vehicle and permanently communicated to the UAV as control commands. We show first results of an implementation of this approach using 360° scanning LiDAR sensors mounted on a mobile sensor unit. The control loop of detection, tracking and guidance of a cooperative UAV in the local environment is demonstrated by two experiments. We show the automatic LiDAR-controlled navigation of a UAV from a starting point A to a destination point B. with and without an obstacle between A and B. The obstacle in the direct path is detected and an alternative flight route is calculated and used.

Proceedings Article | 20 September 2020 Presentation + Paper

A multi-sensorial approach for the protection of operational vehicles by detection and classification of small flying objects

Marcus Hammer, Björn Borgmann, Marcus Hebel, Michael Arens

Proceedings Volume 11538, 1153807 (2020) https://doi.org/10.1117/12.2573620

KEYWORDS: Unmanned aerial vehicles, Sensors, Visible radiation, Cameras, Thermography, LIDAR, Infrared cameras, Infrared imaging, Bolometers, Infrared sensors

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Due to the high availability and the easy handling of small drones, the number of reported incidents caused by UAVs both intentionally and accidentally is increasing. To be capable to prevent such incidents in future, it is essential to be able to detect UAVs. However, not every small flying object poses a potential threat and therefore the object not only has to be detected, but also classified or identified. Typical 360◦ scanning LiDAR systems can be deployed to detect and track small objects in the 3D sensor data at ranges of up to 50 m. Unfortunately, in most cases the verification and classification of the detected objects is not possible due to the low resolution of this type of sensor. In high-resolution 2D images, a differentiation of flying objects seems to be more practical, and at least cameras in the visible spectrum are well established and inexpensive. The major drawback of this type of sensor is the dependence on an adequate illumination. An active illumination could be a solution to this problem, but it is usually impossible to illuminate the scene permanently. A more practical way would be to select a sensor with a different spectral sensitivity, for example in the thermal IR. In this paper, we present an approach for a complete chain of detection, tracking and classification of small flying objects such as micro UAVs or birds, using a mobile multi-sensor platform with two 360◦ LiDAR scanners and pan-and-tilt cameras in the visible and thermal IR spectrum. The flying objects are initially detected and tracked in 3D LiDAR data. After detection, the cameras (a grayscale camera in the visible spectrum and a bolometer sensitive in the wavelength range of 7.5 µm to 14 µm) are automatically pointed to the object’s position, and each sensor records a 2D image. A convolutional neural network (CNN) realizes the identification of the region of interest (ROI) as well as the object classification (we consider classes of eight different types of UAVs and birds). In particular, we compare the classification results of the CNN for the two camera types, i.e. for the different wavelengths. The high number of training data for the CNN as well as the test data used for the experiments described in this paper were recorded at a field trial of the NATO group SET-260 (“Assessment of EO/IR Technologies for Detection of Small UAVs in an Urban Environment”) at CENZUB, Sissonne, France.

Proceedings Article | 23 April 2020 Paper

Image-based classification of small flying objects detected in LiDAR point clouds

Marcus Hammer, Björn Borgmann, Marcus Hebel, Michael Arens

Proceedings Volume 11410, 1141002 (2020) https://doi.org/10.1117/12.2557246

KEYWORDS: Unmanned aerial vehicles, LIDAR, Sensors, Image classification, Cameras, Clouds, Classification systems

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Proceedings Article | 2 May 2019 Paper

UAV detection, tracking, and classification by sensor fusion of a 360° lidar system and an alignable classification sensor

Marcus Hammer, Björn Borgmann, Marcus Hebel, Michael Arens

Proceedings Volume 11005, 110050E (2019) https://doi.org/10.1117/12.2518427

KEYWORDS: Unmanned aerial vehicles, Sensors, LIDAR, Cameras, Clouds, Classification systems, Detection and tracking algorithms, Bolometers, Sensor fusion

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Proceedings Article | 10 May 2018 Paper

Potential of lidar sensors for the detection of UAVs

Marcus Hammer, Marcus Hebel, Björn Borgmann, Martin Laurenzis, Michael Arens

Proceedings Volume 10636, 1063605 (2018) https://doi.org/10.1117/12.2303949

KEYWORDS: Unmanned aerial vehicles, Sensors, LIDAR, Clouds, Target detection, Image segmentation, Cameras

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