See-and-avoid behaviors are an essential part of autonomous navigation for unmanned air vehicles (UAVs). To be fully autonomous, a UAV must be able to navigate complex urban and near-earth environments and detect and avoid imminent collisions. While there have been significant research efforts in robotic navigation and obstacle avoidance during the past few years, this previous work has not focused on applications that use small autonomous UAVs. Specific UAV requirements such as non-invasive sensing, light payload, low image quality, high processing speed, long range detection, and low power consumption, etc., must be met in order to fully use this new technology. This paper presents single camera collision detection and avoidance algorithm. Whereas most algorithms attempt to extract the 3D information from a single optical flow value at each feature point, the authors propose to calculate a set of likely optical flow values and their associated probabilitiesan optical flow probability distribution. Using this probability distribution, a more robust method for calculating object distance is developed. This method is developed for use on a UAV to detect obstacles, but it can be used on any vehicle where obstacle detection is needed. In this paper, they will first review a few different structure from motion methods in section 2. The gradient-based optical flow probability distribution calculation method they use then is discussed in section 3. In section 4, they will present their method for extracting 3D scene information using multiple optical values at each feature point. Results and evaluation are included in section 5 and conclusions and future work are discussed in section 6.
Two-frame structure from motion using optical flow probability disributions for unmanned air vehicle obstacle avoidance
Doppelbildstruktur aus der Bewegung mit Hilfe der Wahrscheinlichkeitsverteilungen des optischen Flusses für die Vermeidung von Hindernissen durch unbemannte Luftfahrzeuge
Machine Vision and Applications ; 21 , 3 ; 229-240
2010
12 Seiten, 9 Bilder, 60 Quellen
Article (Journal)
English
British Library Online Contents | 2010
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