Onboard pattern recognition for autonomous UAV landing

Sung, Chen-Ko; Segor, Florian

doi:10.1117/12.929646

Cited by 6 publications

(4 citation statements)

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“…[9] as well as also [10] concentrate on the autonomous landing of a UAV by using a static ground pattern. These works are showing similar characteristics with our attempts (see [5]). However, exclusively static patterns are used and by that the possibility to adapt for example the size of the pattern and the opportunity to extend the communication with an additional communication channel is gambled away.…”

Section: Related Worksupporting

confidence: 88%

“…The basic functions for adaptive pattern recognition have been reported in [5]. For the pattern recognition, there are two major tasks which must be solved.…”

Section: Onboard Process Chainsmentioning

confidence: 99%

“…Only the automated landing needs the unlimited attention of the user or of a manual steering pilot because the navigation based on GPS is not precise enough for a secure unattended landing. To remove this restriction procedures were developed to provide an on board detection of a ground pattern to use this information for an exact autonomous landing [5].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive pattern for autonomous UAV guidance

Sung

Segor

2013

SPIE Proceedings

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The research done at the Fraunhofer IOSB in Karlsruhe within the AMFIS project is focusing on a mobile system to support rescue forces in accidents or disasters. The system consists of a ground control station which has the capability to communicate with a large number of heterogeneous sensors and sensor carriers and provides several open interfaces to allow easy integration of additional sensors into the system. Within this research we focus mainly on UAV such as VTOL (Vertical takeoff and Landing) systems because of their ease of use and their high maneuverability. To increase the positioning capability of the UAV, different onboard processing chains of image exploitation for real time detection of patterns on the ground and the interfacing technology for controlling the UAV from the payload during flight were examined. The earlier proposed static ground pattern was extended by an adaptive component which admits an additional visual communication channel to the aircraft. For this purpose different components were conceived to transfer additive information using changeable patterns on the ground. The adaptive ground pattern and their application suitability had to be tested under external influence. Beside the adaptive ground pattern, the onboard process chains and the adaptations to the demands of changing patterns are introduced in this paper. The tracking of the guiding points, the UAV navigation and the conversion of the guiding point positions from the images to real world co-ordinates in video sequences, as well as use limits and the possibilities of an adaptable pattern are examined

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Section: Related Worksupporting

confidence: 88%

“…The basic functions for adaptive pattern recognition have been reported in [5]. For the pattern recognition, there are two major tasks which must be solved.…”

Section: Onboard Process Chainsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive pattern for autonomous UAV guidance

Sung

Segor

2013

SPIE Proceedings

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“…In the domain of UAVs and robotic vision, onboard image processing in real-time is one of the key technologies for autonomous operation [19]. Small UAVs have entered a large range of applications as their underlying technology has improved and more possibilities for use have been explored.…”

Section: Autonomous Uavsmentioning

confidence: 99%

TULIPP: Towards ubiquitous low-power image processing platforms

Kalb

Kalms

Göhringer

et al. 2016

2016 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS)

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Abstract-Many industrial domains rely on vision-based applications which require to comply with severe performance and embedded requirements. TULIPP will develop a reference platform, which consists of a hardware system, a tool chain and a real-time operating system. This platform defines implementation rules and interfaces to tackle power consumption issues while delivering high, energy efficient and guaranteed computing performance for image processing applications. Using this reference platform will enable designers to develop a complete solution at a reduced cost to meet the typical embedded systems requirements: Size, Weight and Power. Moreover, for less constrained systems which performance requirements cannot be fulfilled by one instance of the platform, the reference platform will also be scalable so that the resulting boards can be chained for higher processing power. The instance of the reference platform developed during the project will be use-case driven and split between the implementation of: a reference hardware architecture -a scalable low-power board; a low-power operating system and image processing libraries; a productivityenhancing tool chain. It will lead to three proof-of-concept demonstrators across different application domains: real-time and low-power medical image processing product prototype of surgical X-ray system (mobile c-arm); embedded image processing systems within Unmanned Aerial Vehicles (UAVs); automotive real time embedded systems for driver assistance. TULIPP will set up an ecosystem and will closely work with standardization organizations to propose new standards derived from its reference platform to the industry.

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